Housing Bubbles and Busts: The Role of Supply … Bubbles and Busts: The Role of Supply Elasticity Keith Ihlanfeldt and Tom Mayock ABSTRACT. Existing studies of the relationship be-tween

Housing Bubbles and Busts: The Role of Supply Elasticity

Keith Ihlanfeldt, Tom Mayock

Land Economics, Volume 90, Number 1, February 2014, pp. 79-99 (Article)

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Housing Bubbles and Busts: The Role of SupplyElasticity

Keith Ihlanfeldt and Tom Mayock

ABSTRACT. Existing studies of the relationship be-tween housing price dynamics and housing supply—which have relied upon questionable proxies for sup-ply elasticity—have yielded highly mixed results. Inthis paper we provide new evidence on this relation-ship based on actual estimates of the price elasticityof supply for local markets in Florida. Intermarketdifferences in housing supply elasticity are found toexplain much of the variation in housing price move-ments and new construction during the most recentboom-bust cycle. Additionally, we find that variancein the elasticity of housing supply can be attributedto differences in land availability and the local fiscaland regulatory environment. (JEL R14)

I. INTRODUCTION

In recent years there has been considerableinterest in the role that housing supply hasplayed in explaining differences in housingprices and their changes across U.S. housingmarkets. Unfortunately, research on this issuehas been limited by the nonavailability of es-timates of local areas’ supply price elastici-ties. In the absence of supply price elasticities,studies have resorted to using two proxy vari-ables; these proxies have been used by Glae-ser, Gyourko, and Saiz (2008), Huang andTang (2012), and Davidoff (forthcoming).Cox (2011) provides a detailed critique ofthese proxies and finds that both have impor-tant limitations that may result in biased es-timates.1 This may help explain why studies

1 One proxy variable is the measure of natural geograph-ical constraints constructed by Saiz (2010). He estimates theamount of developable land within a metropolitan area,which is defined as the share of land within the area that hasa slope of less than 15 degrees, after excluding land lost tobodies of water. The other proxy variable is the Wharton

Land Economics • February 2014 • 90 (1): 79–99ISSN 0023-7639; E-ISSN 1543-8325� 2014 by the Board of Regents of theUniversity of Wisconsin System

that have used these proxies have yieldedhighly mixed results, with some evidence sug-gesting that the elasticity of supply does notaffect housing market dynamics (Davidoff,forthcoming), but other evidence showing astrong relationship between the cross-sec-tional variation in housing supply conditionsand the severity of housing price and con-struction cycles (Huang and Tang 2012).

The primary purpose of this paper is not torevisit the appropriateness of extant proxiesfor the price elasticity of housing supply, butthe concerns raised by Cox and the mixed na-ture of the extant evidence suggest some newevidence is needed that is not dependent onthe existing proxy variables for the supplyprice elasticity.2 Hence, we take a completely

Residential Land Use Regulatory Index (Gyourko, Saiz, andSummers 2008), which is a measure of the regulatory re-strictiveness within the metropolitan area. Cox offers twomain criticisms of Saiz. First, Cox (2011, p. 14) notes that“Saiz uses an invariant 50 kilometer radius from the urbanfocal point of the metropolitan area to analyze geographicconstraints. Given the huge differentials in the geographicalsizes among the principal urban areas in the sample of themetropolitan areas over 500,000 population, the invariant 50km radius is blunt in the extreme.” Cox’s second criticismpertains to how the Saiz measure treats developed land: “Thesecond problem is that the Saiz natural constraint area doesnot take into consideration the area of existing development(urban area), which by virtue of it being largely occupiedby buildings, also represents a geographical constraint (adevelopment geographic constraint)” (2011, p. 18). Cox’sprincipal criticism of the Wharton index is that “it is basedpartially on loose ‘black box’ opinions and judgments, anddoes not include responses from private housing industryparticipants” (2011, p. 16).

2 However, later in the paper we do compare the abilityof Saiz’s measure of the share of undevelopable land (as anelasticity proxy) and our estimated supply price elasticitiesto explain housing price and quantity changes during Flor-ida’s housing boom and subsequent crash.

The authors are, respectively, professor, Departmentof Economics and DeVoe Moore Center, Florida StateUniversity, Tallahassee; and economist, Office of theComptroller of the Currency, Washington, D.C.

February 2014Land Economics80

different methodological approach toward in-vestigating whether, at the local level, supplyconditions affect price and quantity changesthat occur in response to a demand shock.Specifically, we conduct a two-step longitu-dinal and cross-sectional analysis: First, wemake use of a unique longitudinal database ofsingle-family housing units and their prices toobtain estimates of the short-run supply priceelasticity for each Florida county. Second, weuse these estimates to conduct a cross-sec-tional investigation of the role that interareadifferences in housing supply conditionsplayed in explaining differences in housingprice and construction changes across Floridacommunities during the most recent housingmarket boom and bust periods. The supplyelasticities are found to strongly influenceprice and quantity changes, which providesstrong confirmation of the importance of therole that housing supply plays in explaininghousing market dynamics.

Having found that supply price elasticitiesstrongly influenced price and quantitychanges, we added a second equally importantobjective to this paper; namely, to investigatethe factors that account for differences in sup-ply price elasticities across markets. Apartfrom our findings, this has been recognized inthe literature as an important issue, but it hasreceived limited attention because the datanecessary to undertake such studies are gen-erally unavailable.

II. LITERATURE REVIEW

In this study we examine how differencesin the supply price elasticity of housing(henceforth, supply elasticity) across marketscorrelate with changes in housing prices andquantities that result from positive and nega-tive demand shocks and the factors that ex-plain differences in the supply elasticityacross markets.3 Previous studies that addressthese two issues are reviewed below in sepa-rate sections that discuss the causes of vari-

3 The source of a demand shock may either be a fun-damental change in housing demand (e.g., as caused by achange in income) or a housing bubble (where overoptimis-tic price expectations generate greater demand), which isthen followed by a bust.

ance in supply elasticity and the influence thatthis variance has on the housing market. Thereare also two types of studies in the housingliterature that are related, albeit tangentially,to our current inquiry: those that estimate thesupply price elasticity of housing and thosethat relate housing prices to land use regula-tions. These studies have been thoroughly re-viewed elsewhere and therefore are not dis-cussed here.4

Supply Elasticity and the Housing Market

There are four recent papers that study theeffects that supply elasticity has on housingprices and the amount of new constructionduring booms and busts. The pioneering studyis by Glaeser, Gyourko, and Saiz (2008). Theother three studies, which build on the workof Glaeser, Gyourko, and Saiz, are by Grimesand Aitken (2010), Huang and Tang (2012),and Davidoff (forthcoming). The first twostudies support Glaeser, Gyourko, and Saiz’sconclusion that supply elasticity effects areimportant, while the last study contradicts allprevious studies by concluding that these ef-fects are unimportant.

Glaeser, Gyourko, and Saiz’s inquiry isboth theoretical and empirical. Their theoreti-cal model describes housing bubbles that arisefrom irrational overoptimism and adaptive ex-pectations. The model predicts what supplyand demand analysis would tell us, namely,that inelastic supply leads to greater price in-creases and less new construction in a boomperiod. While a price increase in response toa demand shock may seem more damaging tosocial welfare than a spurt in new construc-tion, Glaeser, Gyourko, and Saiz note that thismay not be the case if excessive new construc-tion distorts otherwise efficient migration de-cisions. The most novel prediction from theirmodel is that the relationship between the sup-ply elasticity and the size of the postbubbleprice correction is theoretically indeterminate.The qualitative relationship between supplyelasticity and the price correction is ambigu-

4 For a review of the estimates of housing supply elas-ticities, see DiPasquale (1999). For a review of the impactsthat regulations have been found to have on housing prices,see Quigley and Rosenthal (2005).

90(1) Ihlanfeldt and Mayock: Housing Bubbles and Busts 81

ous because of two diametrically opposedforces: (1) a higher supply elasticity results ingreater overbuilding during the boom, whichcauses greater excess inventory and largerprice declines in the postboom period; and (2)a higher supply elasticity results in less of aprice rise during the boom, so there is asmaller postboom price correction.

To test their model empirically, GlaeserGyourko, and Saiz proxy for the supply elas-ticity using Saiz’s (2010) measure of devel-opable land within a metropolitan area. Theystudy three distinct periods: two booms(1982–1988 and 1996–2006) and one bust(1989–1996). Price and construction modelsare estimated for each period, and the resultsfrom these models support the hypothesis thatmore inelastic areas have greater price appre-ciation in boom periods. Although weaker, theresults also support the hypothesis that thereis less construction in inelastic areas in boomperiods. The results from the bust period “arenot nearly as robust as those from the boomperiod” (Glaeser, Gyourko, and Saiz 2008, p.208). Modest evidence suggests that there isno relationship between the supply elasticityand the fall in housing prices and that moreelastic markets experienced a larger quantityresponse during the boom period.

Grimes and Aitken (2010) estimate a hous-ing supply model to obtain supply elasticityand a model that registers the responsivenessof prices to a short-run excess demand dis-equilibrium for each of 73 regions in NewZealand, using panel data for 53 quarters.Their key result is obtained using the regionas the observational unit and by regressingtheir price adjustment estimate on the esti-mated elasticity. The estimated coefficient onthe latter variable is negative and statisticallysignificant, indicating that a faster supply re-sponse moderates price spikes following a de-mand shock. While Grimes and Aitken do notemploy the supply elasticity proxies criticizedby Cox (2011), their analysis also suffers froman important measurement issue, namely, theywere able to measure the existing housingstock within each region only at 5-year inter-vals, which forced them to obtain their quar-terly estimates via linear interpolation. Obvi-ously, the interpolation procedure introducesa substantial amount of noise into their hous-

ing data, which casts considerable doubt ontheir estimated elasticities.

Huang and Tang (2012) proxy the supplyelasticity of 200 U.S. cities using the Saiz(2010) measure of developable land and theWharton Residential Land Use Regulatory In-dex (Gyourko, Saiz, and Summers 2008).They find that both proxy variables are linkedto larger booms and busts in housing prices.Huang and Tang measure changes in housingprices using data from Zillow.com. While Zil-low has been shown to yield reasonably ac-curate estimates of the market value of indi-vidual homes, it is not clear that these Zillowestimates can be transformed into a reliableintertemporal price index. The value of theZillow Home Value Index for a given year isthe median of Zillow’s value estimates for allhomes in an area. The major drawback to us-ing this index is that the Zillow value esti-mates are only as good as the “comparisonsales” on which they are based. Because thequality of these comparable sales can varygreatly over time as market conditionschange, the Zillow Home Value Index may besubject to considerable time period–depen-dent measurement error. More importantly,the median value of homes in an area canchange as a result of changes in the stock (e.g.,demolition followed by new construction canraise housing values). As the composition ofthe housing stock can be affected by boomsand busts, Huang and Tang’s results may re-flect changes in either the price of housingservices or the quantity of housing services,which makes it difficult to use their results todraw conclusions about the relationship be-tween price changes and the elasticity ofhousing supply.

Davidoff’s results are strongly inconsistentwith those of the previous three studies, de-spite the fact that he also proxies the supplyelasticity with the Saiz measure and the Whar-ton index. He conducts three tests of the hy-pothesis that the supply elasticity affectedcross-sectional variation in the severity of thehousing price cycles during the 2000s. He ac-knowledges that two of the tests are weak testsbecause of “troubling” assumptions.5 The re-

5 The first test assumes a common national demand


sults from these first two tests are primarily ofinterest because they are consistent with thoseobtained from his final test. The latter in-volved regressing measures of a metropolitanarea’s housing price cycle severity on statefixed effects and three proxies for the area’ssupply elasticity: (1) whether the metropolitanarea is a coastal market (which implies a lowsupply elasticity), (2) Saiz’s measure of de-velopable land, and (3) the Wharton Residen-tial Land Use Regulatory Index. The intentbehind the inclusion of the state fixed effectsis that they capture a “critical source of de-mand heterogeneity” (Davidoff, forthcoming,p. 16). The results indicate that variation inthe supply elasticity proxies is not associatedwith increased cycle severity. Davidoff, there-fore, rejects the hypothesis that differences insupply elasticity caused cross-sectional vari-ation in the severity of the 2000s housing cy-cle among U.S. housing markets.

Determinants of Supply Elasticity

Two papers have investigated the factorsthat explain differences in supply elasticityacross areas (Green, Malpezzi, and Mayo2005; Saiz 2010). Although they take verydifferent empirical approaches, both studiesshare a common conclusion, namely, that theelasticity is lower in metropolitan areas whereland use regulations are more restrictive.

Green, Malpezzi, and Mayo (2005) first es-timate supply elasticities for 45 metropolitanstatistical areas (MSAs) in the United Statesusing annual data for the years 1979–1996. Toobtain their elasticity estimates, Green, Mal-pezzi, and Mayo regress a measure of the per-centage change in the housing stock, which isbased on new building permits, on the laggedfirst difference in natural logarithms of theFannie Mae repeat-sales index of house pricesfor the MSA.6 In the second stage of theirstudy, the estimated elasticity (which is the

shock and the second test assumes that demand and supplyparameters have not changed since the 1980s.

6 Green, Malpezzi, and Mayo’s (2005) justification forlagging their price change variable is that this reduces thepossibility of reverse causation bias. But builders obtainingpermits incorporate current prices into their price expecta-tions, so lagging may have biased their estimates by intro-ducing measurement error.

estimated coefficient on the home price indexfrom stage one) is regressed on a land useregulation index, population density, and con-trols. The regulation index, which the authorsadmit is extremely crude, is the unweightedsum of seven variables that describe the reg-ulatory environment, which were constructedusing answers to survey questions. Resultsfrom the second stage of their analysis showthat elasticities are lower where land use regu-lation is more stringent and population densityis higher, with the latter result presumablycapturing the availability of developable land.

Saiz uses decennial price and quantity dataat the MSA level from the U. S. Census Bu-reau to regress the 30-year changes (1970–2000) in the natural logarithm of housingprices on the same 30-year changes in the nat-ural logarithm of construction costs, the nat-ural logarithm of the housing stock, and thenatural logarithm of the housing stock inter-acted with (1) his measure of developable land(the Saiz measure, as described above), and(2) the Wharton Residential Land Use Regu-lation Index. A higher value of the Whartonindex is interpreted as evidence that land useregulation in the market is more restrictive.The possible endogeneity of changes in thestock and the Wharton index is addressedthrough the use of an instrumental variablesestimator. The results from Saiz’s empiricalmodels show that the supply elasticity variesdirectly with the Saiz measure of land avail-ability and inversely with the Wharton index.7

To summarize the literature, a commonlimitation of the studies of both the conse-quences and causes of supply elasticity is thatthe data used to measure many of the key vari-ables have been suspect. As described below,the database utilized in this study allows forfar more precise measurements of housingprices, the housing stock, and the local regu-latory environment than those used in previ-ous studies. In lieu of using median values,

7 As with Huang and Tang, a possible weakness of Saiz’sempirical methodology is his use of median values to mea-sure housing price changes. Additionally, the Saiz measurepertains to the geography as it existed in 2000, and theWharton index captures the regulatory environment in 2005.To ensure their exogeneity, these variables should have beenmeasured at the beginning of the period (i.e., 1970).


we measure changes in housing prices by con-structing a quality-controlled house price in-dex for each market area. To measure newhome construction, we actually count thenumber of finished homes. Prior studies haveused building permits to measure additions tothe housing stock; these permit-based mea-sures may inaccurately measure new con-struction, particularly during the collapse ofthe housing market, when builders began torevise their expectations of future housing de-mand. To ensure their exogeneity, our land useregulation and available land variables aremeasured prior to the start of our price andconstruction sample. As precise measure-ments of all of these variables are required toreliably estimate supply elasticities and theireffects on housing prices and quantitychanges during boom and bust periods, theempirical results advanced below are alsomore robust and readily interpretable thanwhat is currently found in the literature.

III. DATA

There are three parts to our study: (1) es-timating county supply elasticities, (2) inves-tigating the role these elasticities played in ex-plaining housing price and quantity changeswithin counties during boom and bust periods,and (3) determining the factors that underliedifferences in supply elasticity across coun-ties. Each part of the analysis required assem-bling a different database. For the first part ofthe analysis, we assembled a 21-year panel ofhousing prices and stocks for 63 of Florida’s67 counties that runs from 1990 to 2010.8 Inthe second part of the study, we constructedcross-county databases that included, in ad-dition to the estimated supply elasticities andhousing price and quantity changes, a set ofdemand-side variables matching those em-ployed by Glaeser, Gyourko, and Saiz (2008).The third data set includes county-level vari-ables that are hypothesized to affect the sup-

8 In 3 of Florida’s 67 counties, the number of sales is toolimited or the field reporting the year of construction wastoo unreliable for the county to be used in our analysis. Inone county (Pinellas), severe errors in the construction ofthe digital map made the calculation of the land and regu-latory variables impossible.

ply elasticity, such as measures of the regu-latory and fiscal environment and the amountof developable land.9 Each of these databasesis described in turn below.

Panel Data Sets

The primary data sources used to constructthe panels are the standardized property taxrolls that each county must submit annuallyto the Florida Department of Revenue(FDOR).10 Tax rolls for the years 1995–2011are made available to the public by FDOR.11

The rolls contain two pieces of informationthat make possible our analysis of the causesand consequences of variance in supply elas-ticity. First, each tax roll includes a data fieldindicating the year in which each structurewas built. This variable is used to estimate thesize of the single-family housing stock andsingle-family new construction for each yearof our panel, 1990–2010.12 We utilize the2011 tax roll to generate this supply series.Though the 2011 data is essentially just across-sectional database, we can utilize the in-formation on the timing of development to an-alyze the evolution of the housing stock overtime. More specifically, to estimate the stockof single-family housing in year X, we utilizethe 2011 tax roll to generate a count of all ofthe properties found on the 2011 roll that wereconstructed prior to X. The amount of newconstruction in year X is defined as all of theproperties that were constructed in year X−1.

9 In order to compare the explanatory power of our es-timate elasticity and Saiz’s proxy for elasticity in models ofhousing price and quantity changes, we also built a databasethat involved replicating Saiz’s methodology for calculatingthe percentage of undevelopable land for each county.

10 These tax rolls are used by FDOR to monitor the per-formance of the county tax assessors, who must abide bycertain state statutes in assessing properties within their ju-risdiction. The tax assessors, who are titled “property ap-praisers” in Florida, are elected to office.

11 The year on the tax roll corresponds to the state of thehousing stock in January of that year. For instance, the countof all single-family properties found on the 1996 tax rollrepresents the stock of single-family units as of January 1,1996.

12 A county-level regression of our single-family stockestimate for the year 2000 on the number reported by the2000 census gives us an R-squared of 99.2, a slope of 0.997,and an intercept that is not significantly different from zero.


Because of demolition, some of the housesthat were constructed prior to 2011 may bemissing in our stock and construction counts,especially in the early years of the panel. Toadjust for this removal, we inflated the pre-2010 construction and stock estimates usingdepreciation rates provided by DiPasqualeand Wheaton (1994).13 In the analysis below,models utilizing the depreciation-correcteddata are labeled “ corrected,” whereas modelsutilizing the uncorrected data series are la-beled “standard.”

Properly estimating the price elasticity ofsupply necessitates measuring the movementin the price of housing services. To do this,we utilize the repeat-sales index approach pi-oneered by Bailey, Muth, and Nourse (1963).The first step in constructing the traditionalrepeat-sales index is the estimation of the fol-lowing equation:

N

ln (P )− ln (P ) = D β + u , [1]r s � t t r,st = 0

where denotes the period of the most recentrsale, denotes the period of the second mostsrecent sale, represents the sales price inPkperiod , and denotes the last period in thek Nsample. is a variable that takes on a valueDtof 1 if the most recent sale occurred in period, a value of −1 if the second most recentt

sale occurred in period , and a value of 0totherwise. Equation [1] can be estimated us-ing ordinary least squares (OLS), and the es-timated coefficients can be used to constructa repeat-sales index as follows:

RSI = RSI exp(β ), [2]t 0 t

where is the index value in period ,RSI t βtis the estimated period- coefficient fromtequation [1], and is the value of the in-RSI0dex in the base period, which is traditionallyset equal to 100.

13 DiPasquale and Wheaton (1994) utilize removal ratesof 0.35%, 0.3%, and 0.2% to construct their national series.Because the housing stock in Florida is quite new by nationalstandards and the length of our panel is relatively short, weutilize the 0.2% removal rate for our corrected housing sup-ply series.

One of the great strengths of the repeat-sales approach is the limited amount of datathat is needed for its implementation. Becausethe influence of property characteristics onhousing values is essentially differenced awayin equation [1], the only information requiredto estimate a repeat-sales index is the date andtransaction price of the paired sales. TheFDOR rolls include all of this information, aswell as an indicator of whether the sale wasarm’s length, which allows us to mitigate thepossibility of nonmarket transactions biasingour estimates of housing price movements.

Because repeat sales thin as we go back-ward in time from 1995 (the year of our firsttax roll), we estimate a variant of equation [1]that preserves degrees of freedom by assum-ing that prices change continuously, ratherthan discretely, over time; this assumption al-lows us to estimate the price index more ef-ficiently in time periods with few sales. Thismore flexible sales price index, developed byMcMillen and Dombrow (2001), utilizes theFourier flexible form estimator of Gallant(1981) to estimate a repeat-sales model.14

Specifically, we estimate

ln (P )− ln (P ) = g(T )− g(T )+ u , [3]r s r s r,s

where is defined asg(T )i

2g(T ) = α +α zi 1 2 iQ

+ (γ sin (qz )+λ cos (qz )−1), [4]� q i q iq = 1

where denotes a time variable that is trans-ziformed to lie between 0 and .15 If equals2π Ti1 in the first sale month in the first year of thesample and equals the number ofmax(T)

14 The Fourier flexible form is, in essence, a highly flex-ible smoothing technique that can be used to capture trendseven when sales are thin within time intervals, so long asthere are sales on each side of the interval.

15 Although can, in principle, vary continuously, theTiFDOR data contain fields that report the month and year—but not the day—of transactions. Consequently, we utilizethe month of sale to rescale the time variable using equation[5].


months of sales price observations included inthe sample, is defined as16zi

2πTiz = . [5]i

max(T)

After transforming the time variables, we es-timate the parameters from equation [3] usingOLS, estimating separate models for eachcounty in the sample. Following the estima-tion of equation [3], we utilize the estimatedparameters to trace out price movements overtime. To do this, for each county we first findthe median sales price for single-family unitsin the year 2000; we then use the estimatedcoefficients from equation [3] to estimate thevalue of this representative property in Janu-ary of each year between 1990 and 2010.17

These price variables serve as our measure ofthe price of housing services in our housingsupply models.

As noted above and described below, hous-ing stock–adjustment models are estimated toobtain a supply elasticity for each county.These models, which are based on those es-timated by DiPasquale and Wheaton (1994),require as controls the price of undevelopedresidential lots and construction costs.18 The

16 The number of expansion terms (Q) is chosen to min-imize the Bayesian information criterion statistic, where thenumber of expansion terms was permitted to range from 0to 5.

17 Although we allow for housing prices to evolve incontinuous time when estimating the repeat-sales index, wemust measure time in discrete units in order to estimate thehousing supply equations described below because theFDOR date-of-construction field indicates only the year inwhich the structure was built.

18 Another approach to obtaining estimates of the supplyprice elasticities has been provided by Mayer and Somerville(MS) (2000). Their approach expresses new construction asa function of changes in home prices and costs rather thanas a function of levels of these variables. However, theyargue that the superiority of their model over DiPasqualeand Wheaton’s (DW) (1994) stock-adjustment model resultssolely from the need of DW to construct a housing stockmeasure, which is likely mismeasured in noncensus yearsbecause building permits (which are not always acted upon)are used to measure additions to the housing stock. Becausewe actually count the number of completed homes off of ourtax rolls and do not rely upon permit data, our use of theDW model is not subject to MS’s criticism. While both theDW and MS models are well grounded theoretically, the

former is obtained by estimating price indexesusing the Fourier flexible form in the samemanner as described above, using turnover inthe residentially zoned, vacant lots that appearon the tax rolls. To estimate the physical costof construction, we first calculate the medianvalue of physical improvements to land forsingle-family properties using the replace-ment cost variable from the 2000 FDOR taxroll. We then utilize the construction cost se-ries published by the RS Means Company, acommercial vendor that researchers in the pasthave used to investigate the cost of construc-tion, to estimate the value of this representa-tive structure annually from 1990 to 2010.19

Lastly, the price, land, and construction costseries are expressed in 2010 dollars using theCPI-less-shelter index published by the Bu-reau of Labor Statistics.

County Cross-Sectional Database Used toStudy Consequences of Supply Elasticity

In addition to boom and bust period hous-ing price and quantity changes calculatedfrom our panel data and our estimated elastic-ities, our data set includes the same demandvariables used by Glaeser, Gyourko, and Saiz(2008): the percentage of college graduates,average January temperature, personal in-come, income growth, and precipitation.20

They measure these variables at the MSAlevel, while we measure them for each Floridacounty.

estimation of the MS model is much more data intensive.The MS model includes current changes in prices and costs,along with lagged changes, as explanatory variables. Be-cause they are endogenous, current changes must be instru-mented, which puts heavy demands on MS’s data that gobeyond what is possible with our data.

19 For instance, if the construction value in 2000 is$100,000, and the RS Means index increases 10% between2000 and 2001, our 2001 construction cost estimate will be$110,000.

20 To quote Glaeser, Gyourko, and Saiz (2008, p. 207),“While these variables [temperature and precipitation] donot change over time, demand for them might have, so theyprovide a natural way of controlling for changes in demand.”Later in the same paragraph they say, “The actual level ofeducational achievement and income are static, of course,and should only matter if they are correlated with changesin local economies.”


County Cross-Sectional Database Used toStudy Determinants of Supply Elasticities

To estimate models explaining differencesin supply elasticities across counties, we needdata on factors that influence a developer’sability to produce more housing in responseto increases in the price of housing. Such fac-tors include the local regulatory environment,the availability of developable land, and thecurrent fiscal situation of the county.

We hypothesize that supply elasticity ishigher in counties that have less restrictiveland use regulations and a larger amount ofpotentially developable land. Additionally,because local governments have an incentiveto block developments that could strain thepublic coffer, we expect the elasticity of hous-ing supply to be higher in locales where theadditional tax revenue from development ismore likely to offset the concomitant increasein the demand for public services than to gen-erate a fiscal deficit.

In Florida, land use regulation is primarilythe responsibility of county governments. Insome other states, especially those in theNortheast and Midwest regions, land useregulation is chiefly a municipal governmentfunction. This raises the issue of how relevantour results may be to other states. Within theSouth, Florida’s structure is not unique.County governments in the Southern statestend to have more power and service respon-sibility than counties in other regions. In manyways, counties in the South are more like mu-nicipalities than counties in other regions ofthe United States. The land use powers ofFlorida counties emanate from the GrowthManagement Act of 1985 and do not resultfrom constitutional provisions. Hence, as inthe case of cities, Florida counties regulateland on the basis of local legislation. Also ofinterest is that a majority of Florida’s popu-lation lives within the unincorporated areas ofcounties.

To measure the restrictiveness of land useregulation, we make use of two sources ofdata. First, following Ihlanfeldt (2009), weuse the county’s expenditures on comprehen-sive planning. These expenditures are re-ported by the county and each city within thecounty annually to the Florida Department of

Financial Services. These expenditure mea-sures include spending related to land useplanning and the enforcement of regulationsdesigned to implement the plans. One of theadvantages of this variable is that it respondsto the concern—expressed by Fischel (1989)and Quigley and Rosenthal (2005)—that a lo-cal government’s statutory land use regula-tions may correlate only weakly with the ac-tual level of restrictiveness, given thatplanners’ enforcement and interpretation ofthe regulations are highly discretionary.21

What is actually spent on land use regulationsmay better reveal the government’s impact onland markets in practice. Thus, we hypothe-size that supply elasticity is lower in thosecounties where the local government is spend-ing more to affect land use.

The second measure of land use regulationrestrictiveness is from Mayock (2009). Heemploys the future land use maps that juris-dictions must submit to the Florida Depart-ment of Community Affairs in order to com-ply with Florida’s Growth Management Act.These maps specify the number of housingunits that can be constructed on the landwithin the jurisdiction. For each county, ameasure akin to a minimum lot size require-ment is calculated as the total acreage of un-developed land that is classified as “residen-tial land” by FDOR in the 2011 tax rolldivided by the total number of housing unitsallowed under the future land use map.22 Min-imum lot size requirements are generally en-forced and their interpretation is straightfor-ward. Moreover, these requirements are themost frequently cited type of regulation thatlocal jurisdictions use to exclude unwanteddevelopment (Quigley and Rosenthal 2005).

21 Planners’ discretion is empirically examined byCheung, Ihlanfeldt, and Mayock (2009), whose evidencesuggests that developers’ regulatory costs are best describedas negotiated outcomes between them and planners.

22 In the future land use maps, each parcel in the stateof Florida is assigned a range of allowable housing units. Toconstruct our minimum lot size measure, we first calculatethe maximum number of units that are allowed on each par-cel using the midpoint of the allowable range that is indi-cated on the future land use map. The minimum lot sizemeasure is then calculated by dividing the total area of un-developed land in each county by the maximum number ofhousing units allowed on undeveloped land.


Our measures of land use regulation aredifferent from those employed in previousstudies. Almost without exception, these stud-ies have used a count variable to measureregulation, where the number of specific pol-icies or regulations (out of a given maximumpossible) is summed, with a higher total in-terpreted as implying greater restrictiveness.The issue, of course, with count indexes ishow to weight the importance of various reg-ulations in forming the sum. There is little ba-sis for the weights that have been used in theexisting literature.

To measure the quantity of undevelopedland within each county that is potentially de-velopable, we start with the universe of par-cels in the county and first eliminate all landon which major improvements (e.g., homes,commercial property) are located. We theneliminate all parcels that are not classified byFDOR as residential land.23 Lastly, we elim-inate all parcels that are held by public entities(e.g., national parks). The total acreage on theparcels remaining after this successive elimi-nation process is summed to generate our ag-gregate measure of undeveloped land.

It should be emphasized that our definitionof developable land is much more narrow thanwhat has previously been adopted in the lit-erature. Although all land can, in principle, bedeveloped or redeveloped, it is much easierfor developers to construct new housing onparcels that do not require any demolitionwork and have already been approved for resi-dential use.24 Moreover, if developers target

23 Although the FDOR classification does not corre-spond directly to municipal-level zoning classifications,FDOR officials informed us that these parcels are generallyunimproved lots that are currently zoned for residential use.The residential classification is composed of 10 subclassi-fications: vacant residential, single-family, mobile homes,multifamily with 10 units or more, condominiums, cooper-atives, retirement homes, miscellaneous residential, multi-family with less than 10 units, and an “undefined” classifi-cation that is used at FDOR’s discretion.

24 For instance, many of the central business districts inthe counties in our sample have very high allowable densi-ties, and we expect a higher supply elasticity in areas thathave less stringent land use regulations. As would be ex-pected, however, very large proportions of the land in theurban core have already been developed. It is thus highlyunlikely that the land use regulations that apply to such par-cels would influence the supply of new single-family hous-ing, because profit-maximizing developers have a strong in-

residentially zoned land for new constructionprojects, the land use regulations on such par-cels should influence the pattern and timingof development more so than regulations thatfall on other types of land that are less ame-nable to new development. Because we seekto explain the variance in the short-run supplyelasticity, we focus on the availability ofland—and the density restrictions on suchland—on which developers can build mostrapidly.

We use two variables to capture the fiscalenvironment. The first variable is the coun-tywide millage rate that is used to calculatead valorem property tax bills. We hypothesizethat counties with higher millage rates willhave higher supply elasticities because localofficials are more likely to approve develop-ment that generates tax revenue that exceedsthe increase in expenditures on public servicesassociated with the development. For in-stance, consider a proposed development thatwill be assessed at dollars and will consumeV

additional dollars in public services. If theBcurrent tax rate is , this proposed develop-τment will generate a fiscal surplus if .τV > BThis surplus would allow local officials to ei-ther increase expenditures on public servicesor lower the tax rate; neither of these actionswould be likely to draw political backlashfrom incumbent citizens and NIMBY groups,and rational politicians and planners wouldhave strong incentives to support the new de-velopment. On the other hand, if andτV < Bthe new development generates a fiscal deficit,local officials will be faced with the politicallyunpalatable choice between raising propertytaxes on both new and existing residents orreducing the level of public service provision.Thus, in the case where a new developmentgenerates a fiscal deficit, local officials havestrong incentives to block the new develop-ment. For a fixed level of public services, it isclear that the probability that a new develop-ment generates a fiscal surplus increases withthe millage rate ( ). Because the short-runτsupply response to price increases is a func-tion of the rate at which new developments

centive to eschew the costly demolition of high-valuestructures and to target development toward parcels withoutmajor improvements.


can be approved, we expect the short-run sup-ply elasticity to be higher in locations withhigher millage rates.

Another variable that influences the fiscalimpact of new development is the value of thecounty’s existing property. It is straightfor-ward to show that if the marginal cost of pro-viding public services is constant and the av-erage assessed value of the new developmentis lower than that of existing property, the mil-lage rate must rise in order to provide thesame level of public services per household.For reasons described above, politically astutelocal officials will be inclined to prohibit ordelay development projects that they believewill reduce the average value of property inthe jurisdiction. This screening out of low-val-ued projects results in an inverse relationshipbetween the supply elasticity of housing andthe value of existing property. To test this hy-pothesis, we include among the regressors theaverage estimated market value of existingsingle-family structures. We focus on thevalue of single-family units as opposed to thevalue of all real estate because the demand forpublic services, which is the primary deter-minant of the millage rate, is driven by theexisting residents of the community, many ofwhom own single-family properties.

Because the regulatory and local fiscalvariables will generally change as new devel-opment occurs, the inclusion of such variablesthat are measured in the same time period thatis used to calculate the elasticity measurescould introduce simultaneity bias in the re-gressions that investigate the determinants ofelasticity. To avoid such bias, our availableland measure, minimum lot size measure, andfiscal variables are calculated in 1990, theyear the panel used to calculate the elasticityof supply starts.25 Because the municipal ex-penditure data is unavailable before 1993, ourmeasure of comprehensive planning expen-ditures is measured in 1993.

25 Because the FDOR tax rolls are not available in digitalformat before 1995, to calculate the average value of single-family housing in 1990, we first calculate the average valueof single-family housing using the 1995 tax roll. We thenuse the single-family repeat-sales index to back forecast thismean to its 1990 value.

Constructing the Saiz Proxy for FloridaCounties

To construct the Saiz measure at the countylevel in Florida, we first used GIS technologyto overlay a 60 m by 60 m grid on each countyin the state.26 Each square on the grid is as-signed a slope value using the 2011 U.S. Geo-logical Survey (USGS) Digital ElevationModel map and a land use code from the 2001USGS National Land Cover Dataset.27 A gridpoint is classified as undevelopable if its slopeis greater than 15 degrees or the land use codeindicates that the grid point is water, woodywetlands, or emergent herbaceous wetlands.28

Following the classification of each grid point,we define the percentage of undevelopableland as the area of all undevelopable gridpoints in the county divided by the area of allgrid points (developable and undevelopable)in the county.

IV. METHODOLOGY AND RESULTS

To estimate a supply elasticity for eachcounty, housing supply stock–adjustmentmodels similar to that of DiPasquale andWheaton (1994) are estimated. These models,along with the results from their estimation,are described below. We then describe themodels and results that comprise our analysisrelating boom and bust changes in housingprices and quantities to measures of supplyelasticity. Lastly, we explore factors account-ing for intercounty differences in elasticityand estimate models in which the Saiz proxyfor the supply elasticity is used in lieu of ourelasticity estimate.

26 The main difference between our construction of thedevelopable land proxy and that of Saiz is one of granularity;whereas we utilize an overlay where each piece of the gridis a 60 m by 60 m square, Saiz (2010, p. 1254) utilizes agrid of 90 m by 90 m squares. Hence, our sampling is at afiner level than that of Saiz.

27 The raw land cover and elevation data can be down-loaded using the USGS Seamless Data Warehouse, which isavailable at http://seamless.usgs.gov/.

28 The water, woody wetlands, and emergent herbaceouswetlands classifications correspond to land use codes 11, 90,and 95, respectively, on the 2006 USGS National LandCover Dataset.


Housing Supply Stock–Adjustment Models

To estimate a supply elasticity for eachcounty, the following stock-adjustment modelwas estimated:29

C = α +α P +α I +α LANDt 1 2 t 3 t 4 t

+α COST −α S + ε , [6]5 t 6 t −1 t

where Ct is the number of single-familyhomes completed in year t, Pt is the housingprice in year t, It is the average constructionfinancing interest rate in year t, LANDt is theland price in year t, COSTt is the contructioncost in year t, St is the stock of single-familyhousing in year t, and εt is the error term. Fol-lowing DiPasquale and Wheaton (1994), ourmethod of estimation was OLS. It should beemphasized that because of the durable natureof housing, in equilibrium it is the price ofhousing and the total stock of housing—notthe level of construction—that are simulta-neously determined; OLS estimates of equa-tion [6] should thus not be plagued by simul-taneity bias.30

29 Our estimated supply models are an improvementover those estimated by DiPasquale and Wheaton in that weare able to better measure a number of the variables: (1)their dependent variable is permits, while ours is actualhomes built; (2) they use the average price of farmland astheir land cost measure, while we use a quality-controlledresidential land price index; and finally (3) their stock mea-sure is estimated by updating census data with permits, whileours is the actual number of single-family homes on the taxroll.

30 Topel and Rosen (1988) and Mayer and Somerville(2000) estimate housing construction models in which thehousing price term is treated as an endogenous variable. Nei-ther of these studies, however, provide estimates from abenchmark OLS model or perform any statistical tests thatprovide evidence that construction and housing prices aresimultaneously determined. Furthermore, Topel and Rosenargue that any such bias should be minimal: “Endogeneity[in a housing construction model] is unlikely to be a seriousproblem because investment is such a small fraction of ex-isting stock” (1988, p. 728). Because of the well-known poorperformance of instrumental variables models in small sam-ples and the absence of empirical evidence indicating thatsimultaneity bias is a problem, we utilize OLS to estimateequation [6]. Another possible concern raised by equation[6] is that falling prices may cause construction to fall tozero, in which case our dependent variable would be cen-sored, possibly biasing our estimator. However, while con-struction did substantially fall in many counties as pricesplummeted, in no case did new construction totally cease.

The short-run elasticity is equal to

Ptε = α = , [7]SR 2

Ct

where and are the average annual hous-P Ct ting price and average annual level of con-struction over the course of the panel.31,32

Summary measures for the estimated sup-ply elasticities, along with summary measuresfor the variables used in the rest of our anal-ysis, are reported in Table 1. The averageshort-run price elasticity in our sample wasapproximately 2, with a maximum of approx-imately 8.33 A comparison of the correctedand uncorrected elasticity terms suggests that,at least on average, the estimated short-runelasticities are relatively insensitive towhether the removal-rate correction is utilizedin the construction of our housing supply se-ries. Regarding price movements, the averagecounty in our sample experienced real single-family price appreciation of approximately89% between 2000 and 2006; this dramatic

31 The long-run supply elasticity, which measures theresponsiveness to changes in the total stock of housing toprice changes, is equal to . As the fo-ε = (α /α )/(P /C )LR 2 6 t tcus of this paper is on construction activity and short-runprice changes, our analysis focuses on the short-run elastic-ity of supply.

32 Previous work (e.g., Rosenthal 1999; Mayer and Som-erville 2000) has found that housing starts are stationary,whereas a number of the independent variables in our stock-adjustment equation are nonstationary. Should this be thecase, equation [6] could possibly be an unbalanced regres-sion “in which the regressand is not of the same order ofintegration as the regressors, or any linear combination ofthe regressors” (Banerjee et al. 1993, p. 164). In such un-balanced regressions, standard distributions cannot be usedto conduct inference. Researchers wishing to conduct infer-ence on unbalanced regressions have generally resorted toone of two approaches that allow for the use of standarddistribution theory: transforming the independent variablesso that they are integrated of the same order as the regres-sand or constructing a set of cointegrated regressors. Mayerand Somerville (2000) adopt the former approach, whereasGrimes and Aitken (2010) adopt the latter. As we make noattempt to conduct inference on the parameters of the stock-adjustment equation, we do not adopt either of those ap-proaches here.

33 An appendix is available upon request that containsthe estimated price coefficient, its estimated standard error,and the implied supply price elasticity obtained from esti-mating the stock-adjustment model for each of the 64 coun-ties.


TABLE 1Summary Statistics

Variable Description Mean

Bubble appreciation Real price appreciation: 2000–2006 89.00 (32.66)Crash appreciation Real price appreciation: 2007–2010 −36.94 (18.12)Short-run elasticity Short-run price elasticity of supply 2.07 (2.02)Corrected short-run

elasticityDepreciation-corrected short-run price

elasticity of supply2.03 (1.98)

Bubble construction Single-family construction: 2000–2006 14,797.62 (17,753.75)Corrected bubble

constructionDepreciation-corrected single-family

construction: 2000–200614,994.59 (17,991.13)

Crash construction Single-family construction: 2007–2010 3,209.00 (3,666.87)Corrected crash

constructionDepreciation-corrected single-family

construction: 2007–20103,222.21 (3681.89)

Total housing units Total housing units in 2000 108,083.00 (167,058.30)Land area Land area in square miles 825.45 (386.41)% College graduates Percentage of residents with a college

degree17.08 (8.04)

Income per capita Real income per capita in 2000 24,355.79 (7,132.74)Income growth Real income growth between 2000 and

20063,240.60 (2,708.66)

Rainfall Average annual rainfall 53.79 (5.24)January temperature Mean January temperature 58.16 (4.54)Planning

expendituresComprehensive planning expenditures in

19931,913,983.00 (3,368,927.00)

Minimum lot size Minimum lot size on undevelopedresidential land in 1990

0.89 (0.69)

Undeveloped land Acres of undeveloped residential land in1990

42,788.55 (27,948.03)

Millage Countywide millage rate in 1990 16.89 (2.41)Average housing

valueAverage value of single-family housing

in 199058,435.30 (28,818.90)

Note: Standard deviations in parentheses. Observations = 63.

price swing was accompanied by, on average,about 15,000 new single-family homes (anannualized rate of approximately 2,100 units).After reaching a peak around 2007, housingprices in Florida began a precipitous decline,with a mean rate of appreciation between2007 and 2010 of −37%. As expected, thecollapse of housing prices was met with a con-comitant reduction in construction activity,with the mean annualized rate of new single-family construction between 2007 and 2010falling to just above 800.

The comovement of single-family housingprices and construction and land costs for sixof the most populous counties in Florida isdisplayed in Figure 1.34 These plots, which are

34 Similar graphs for all of the other counties in our sam-ple are available upon request. Pinellas County is actuallythe fifth-largest county in Florida by population. Because ofsome serious problems in the way that the digital parcel map

constructed using the cost and price series de-scribed above, reveal that, prior to 2000 (theyear that we use to mark the start of the hous-ing bubble, which is delineated with a verticalbar), the price of single-family housingroughly tracked movements in the physicalcosts of construction. From 2000 through2006, however, there was an incredibly sharpdivergence in housing values and constructioncosts. Movements in land prices, which arealso susceptible to speculation, followedhousing price movements closely over thistime period.

Starting around the year 2007 (the year thatwe define as the end of the bubble, which ismarked with a vertical bar), housing pricesand land prices began to fall sharply. Con-

was formatted in Pinellas, data from this county could notbe used for the sample.


FIGURE 1Home Values, Land Costs, and Construction Costs in Florida, 1990–2010

struction costs, on the other hand, continuedthe slow increase that they had experiencedthroughout the course of the last two decades.

Comparing the counties in Figure 1, we seethat although the bubble began and endedaround the same time in each of the six coun-ties, the magnitudes of the price swings duringthese periods were not the same throughoutthe state. The three counties in southern Flor-ida (Dade, Broward, and Palm Beach) sawmuch more dramatic price increases than didthe counties in central Florida (Hillsboroughand Orange) and north Florida (Duval), andthe postbubble corrections have subsequentlybeen more severe in southern Florida. Thehousing bubble thus clearly manifested itselfin different forms in different housing mar-kets, and one of the goals of this study is toinvestigate the role of supply elasticity in gen-erating these differences. We turn now to thisinvestigation.

The Relationship between Supply Elasticityand Changes in Housing Prices andQuantities

Like Glaeser, Gyourko, and Saiz (2008), toexplore the effect that supply elasticity has on

changes in housing prices during boom andbust periods, we begin by estimating simplebivariate cross-sectional regression equations.The dependent variables are the countychange in (1) housing prices between 2000and 2006, and (2) housing prices between2007 and 2010. The single explanatory vari-able is the short-run estimated supply elastic-ity.35 The variation that is used to estimatethese models is displayed in Figure 2, whichreveals that there was a large amount of var-iance in price appreciation over this periodthroughout the state. The most dramatic ap-preciation during these years was concen-trated in the southern counties, with pricegrowth particularly robust along the coast. Interms of housing supply, counties in southernFlorida are also characterized by low supplyelasticity relative to many counties in the cen-tral and northern parts of the state. The clus-tering of high appreciation rates in areas oflow supply elasticity shown in Figure 2 and

35 Our bivariate models are analogous to those estimatedby Glaeser, Gyourko, and Saiz (2008), except that they mea-sured price and quantity changes at the MSA level and, inlieu of using the supply elasticity, they use Saiz’s (2010)proxy for supply elasticity.


FIGURE 2Price Appreciation and Supply Elasticity in Florida,

2000–2006

the bivariate regression results reported inColumn 1 and Column 3 of Table 2 (showingthe elasticity impact to be negative and statis-tically significant) bear out the theoretical pre-dictions of Glaeser et al.36

As noted by Glaeser, Gyourko, and Saiz(2008), the biggest challenge to any implica-tion that might be drawn from simple bivariatemodels is that they reflect demand-side, ratherthan supply-side, differences across markets.We therefore follow Glaeser, Gyourko, andSaiz by adding to our estimated models theirdifferent measures of housing demand as con-trols: mean January temperature, mean annualprecipitation, share of population with collegedegrees in 2000, the level of real income in2000 as measured by the Bureau of EconomicAnalysis, and the growth in that income be-tween 1990 and 2000. An inspection of Col-umn 2 and Column 4 of Table 2 provides

36 There may appear to be an endogeneity bias in theseresults. Changes in prices are explained by estimated elas-ticities, which are obtained by relating new construction tochanges in price levels. Indeed, if both the elasticity andprice (quantity) change models were estimated using lon-gitudinal county-level data, it would be hard to argue thatour elasticity estimates are exogenous to the boom and bustprice and quantity changes we seek to explain. However,our price and quantity change models are cross-sectional,which allays possible endogeneity concerns.

strong confirmation that counties with highershort-run supply elasticities experienced sub-stantially less price appreciation during the2000–2006 period, even when controlling fordemand-side shocks. The coefficients on theestimated elasticity terms in Table 2 suggestthat a one-standard deviation increase in theshort-run supply elasticity—an increase ofabout 2—leads to a decrease in real appreci-ation over this period of between 7% and12%. The impact of supply elasticity on ap-preciation is far more pronounced for moredramatic changes in the supply environment.For instance, our estimates suggest that an in-crease in the supply elasticity from 0 to 8 (ap-proximately the maximum in the sample)would have reduced price appreciation duringthe housing boom by between 29% and 48%.

As noted in Section 2, in the model ofGlaeser , Gyourko, and Saiz, the qualitativerelationship between supply elasticity and thepostbubble price correction is ambiguous. Theopposing effects that Glaeser, Gyourko, andSaiz identify appear to be off-setting in oursample. In Figure 3, which depicts the supplyelasticity and evolution of housing prices be-tween 2007 and 2010, there appears to be norelationship between supply elasticity and thepostbubble price corrections, as some high-elasticity markets experienced price declinesthat exceeded the price correction in marketswith much less elastic housing supply. Bivar-iate and multivariate regressions (reported inTable 3) in which price appreciation between2007 and 2010 is the dependent variable alsofail to reveal any meaningful statistical rela-tionship between post-2006 housing price dy-namics and housing supply conditions: thesign on the short-run elasticity coefficient var-ies across specifications in Table 3, and thecoefficients are never statistically significant.

To explore the effect that supply elasticityhas on changes in housing output duringbooms and busts, we again follow Glaeser,Gyourko, and Saiz (2008). The dependentvariables are new construction measured overthe periods (1) 2000–2006 and (2) 2007–2010. The simple model includes as explan-atory variables the supply elasticity and thesame controls as Glaeser, Gyourko, and Saizincluded: the natural logarithm of land areaand the natural logarithm of the housing stock


TABLE 2Bubble Appreciation Regressions, Dependent Variable: Price Appreciation: 2000–2006

Housing Supply Measure

Standard Standard Corrected CorrectedVariable (1) (2) (3) (4)

Short-run elasticity −5.897*** (1.652) −3.593*** (1.332) −6.010*** (1.684) −3.645*** (1.353)% College graduates −0.1577 (0.3795) −0.1621 (0.3798)Income per capita 0.000205 (0.000612) 0.000209 (0.000612)Income growth 0.00360** (0.00140) 0.00360** (0.00140)Rainfall 1.395** (0.578) 1.392** (0.578)January temperature 4.040*** (0.695) 4.037*** (0.696)Constant 101.2*** (6.566) −227.5*** (47.61) 101.2*** (6.570) −227.3*** (47.76)Observations 63 63 63 63R-squared 0.134 0.615 0.133 0.614

Note: Robust standard errors in parentheses.** p<0.05; *** p<0.01.

FIGURE 3Price Appreciation and Supply Elasticity in Florida,

2007–2010

in 2000. The second specification of themodel adds the demand variables. Results arereported in Table 4 and Table 5 for the bubbleand crash regressions, respectively.

The regression results in Table 4 reveal thatcounties in which the price elasticity of supplyis higher experienced a much more substantiallevel of building activity during the housingbubble than counties with lower price elastic-ities. Again, the regression results are robustto the inclusion of the demand variables. Theresults from estimating the crash constructionregressions are presented in Table 5. These

results show that there was a larger quantityresponse in counties with higher short-runsupply elasticities. Once again, the results arehighly insensitive to whether the models in-clude the demand variables. All of these hous-ing output results are consistent with the theo-retical predictions of Glaeser, Gyourko, andSaiz (2008).

Explaining Differences in Supply Elasticitiesacross Counties

The above results showing the importantrole played by the supply elasticity in explain-ing housing price and quantity changes acrosscounties beg the question: What explainscross-county differences in the supply elastic-ity? We have hypothesized that the ability ofthe construction industry to respond to an in-crease in housing prices is hampered by landuse regulations, a scarcity of developableland, and fiscal conditions that render localofficials reluctant to grant approval for devel-opment projects. We therefore estimated mod-els where the dependent variable is the countysupply elasticity and the independent vari-ables capture the aforementioned factors.

We have two alternative measures of landuse regulation: the minimum lot size on va-cant residential land in 1990 and comprehen-sive planning expenditures in 1993. Our mea-sure of developable land is the amount ofland, measured in acres, that had not been de-veloped by 1990 and was classified by FDOR


TABLE 3Crash Appreciation Regressions, Dependent Variable: Price Appreciation: 2007–2010



Short-run elasticity 0.733 (1.082) −0.136 (0.896) 0.744 (1.102) −0.153 (0.913)% College graduates −0.3741 (0.3791) −0.3737 (0.3790)Income per capita 0.000110 (0.000603) 0.000110 (0.000604)Income growth 0.000565 (0.000832) 0.000564 (0.000834)Rainfall −0.466 (0.348) −0.467 (0.348)January temperature −2.855*** (0.492) −2.856*** (0.492)Constant −38.46*** (3.693) 156.3*** (32.92) −38.45*** (3.696) 156.5*** (32.91)Observations 63 63 63 63R-squared 0.007 0.473 0.007 0.473

Note: Robust standard errors in parentheses.*** p<0.01.

TABLE 4Bubble Construction Regressions, Dependent Variable: ln(New Construction: 2000–2006)



Short-run elasticity 0.131*** (0.0471) 0.153*** (0.0561) 0.135*** (0.0475) 0.158*** (0.0564)% College graduates 0.0075 (0.020) 0.00771 (0.0202)Income per capita 2.32e–05 (2.59e–05) 2.31e–05 (2.57e–05)Income growth 4.07e–06 (5.43e–05) 4.22e–06 (5.38e–05)Rainfall −0.00792 (0.0207) −0.00759 (0.0206)January temperature 0.00477 (0.0318) 0.00526 (0.0316)ln(Total housing units) 1.261*** (0.0611) 1.132*** (0.110) 1.262*** (0.0608) 1.131*** (0.109)ln(Land area) −0.498* (0.259) −0.417 (0.265) −0.499* (0.259) −0.418 (0.265)Constant −1.970 (1.622) −1.735 (2.216) −1.959 (1.621) −1.755 (2.207)Observations 63 63 63 63R-squared 0.873 0.881 0.873 0.882

Note: Robust standard errors in parentheses.* p<0.10; *** p<0.01.

TABLE 5Crash Construction Regressions, Dependent Variable: ln(New Construction: 2007–2010)



Short-run elasticity 0.154*** (0.0466) 0.165*** (0.0491) 0.159*** (0.0473) 0.170*** (0.0496)% College graduates 0.00965 (0.0174) 0.00983 (0.01727)Income per capita −2.31e–06 (2.63e–05) −2.42e–06 (2.61e–05)Income growth 3.70e–05 (5.10e–05) 3.71e–05 (5.05e–05)Rainfall −0.0180 (0.0158) −0.0177 (0.0157)January temperature −0.000369 (0.0251) 9.72e–05 (0.0250)ln(Total housing units) 1.009*** (0.0523) 0.952*** (0.0891) 1.009*** (0.0521) 0.952*** (0.0888)ln(Land area) −0.360* (0.201) −0.324 (0.205) −0.361* (0.201) −0.325 (0.204)Constant −1.479 (1.282) −0.383 (1.794) −1.477 (1.281) −0.408 (1.788)Observations 63 63 63 63R-squared 0.876 0.886 0.876 0.887

Note: Robust standard errors in parentheses.* p<0.10; *** p<0.01.


TABLE 6Elasticity Regressions, Dependent Variable: Short-Run Elasticity


Standard CorrectedVariable (1) (2)

Minimum lot size −0.898*** (0.297) −0.878*** (0.290)Undeveloped land 2.29e–05*** (8.40e–06) 2.25e–05*** (8.23e–06)Planning expenditures −1.39e–07** (5.50e–08) −1.38e–07** (5.41e–08)Average housing value −1.69e–05** (6.75e–06) −1.64e–05** (6.59e–06)Millage 0.199* (0.103) 0.195* (0.101)Constant −0.218 (2.034) −0.219 (1.993)Observations 63 63R-squared 0.217 0.217

Note: Robust standard errors in parentheses.* p<0.10; ** p<0.05; *** p<0.01.

TABLE 7Influence of Policy Variables on Price Appreciation during Bubble

A 1 Std. Dev. Increase in Elasticity Change Bubble Appreciation Change

Minimum lot size 0.62 unit decrease ⇒ 3.7% increaseUndeveloped land 0.64 unit increase ⇒ 3.8% decreasePlanning expenditures 0.46 unit decrease ⇒ 2.8% increaseAverage housing value 0.48 unit decrease ⇒ 2.9% increaseMillage 0.48 unit increase ⇒ 2.9% decrease

as residential land in 2011. The variables weuse to capture local fiscal conditions are the1990 countywide real property millage rateand the average value of single-family homesin 1990.

Results from these regression models aredisplayed in Table 6. The results show thatcounties with larger minimum lot size require-ments are found to have lower short-run sup-ply elasticities. The amount of developableland is also found to play an important role indetermining the short-run supply response toprice increases, as areas with more develop-able land have higher short-run elasticities.The estimated coefficients on the planning ex-penditure terms in Table 6 are all negative andare statistically significant. These results pro-vide some evidence that counties that spendmore money implementing and enforcingland use regulations have lower short-run sup-ply elasticities. Turning to the fiscal variables,the positive and statistically significant coef-ficient on the millage rate term is consistentwith the hypothesis that developers are lessconstrained by local officials when the taxrevenue generated by new development ismore likely to exceed the increase in expen-

ditures on public services associated with thedevelopment. The negative and statisticallysignificant coefficient on the housing valueterm is also consistent with the conjecture thatthe strategic approval of fiscally advantageousprojects influences the elasticity of supply ofhousing, as local officials have strong incen-tives to prohibit or delay projects that mayerode the average value of property in the ju-risdiction.

The results from Table 6 can be used toinvestigate how characteristics of the localeconomic environment influenced price ap-preciation and new construction during themost recent housing boom and bust cycle.One such exercise is reported in Table 7,where we utilize the elasticity coefficientsfrom Table 2 to study how local factors influ-enced price appreciation during the bubblethrough the supply elasticity channel. The re-sults reveal that relatively small differences inthe variables influencing supply elasticity re-sult in nontrivial differences in price appre-ciation. From a public policy perspective,these results suggest that even moderatechanges in a local community’s regulatory en-vironment and fiscal conditions can have a


TABLE 8Appreciation Regressions with Saiz-Type Supply Measure, Dependent Variable: Price Appreciation

Time Frame

2000–2006 2000–2006 2007–2010 2007–2010Variable (1) (2) (3) (4)

% Undevelopable land 118.8*** (16.41) 56.15** (25.76) −48.68*** (9.477) −14.67 (13.87)% College graduates −0.205 (0.327) −0.402 (0.370)Income per capita 0.000108 (0.000817) 0.000159 (0.000599)Income growth 0.00347* (0.00176) 0.000711 (0.000864)Rainfall 1.373** (0.568) −0.424 (0.354)January temperature 3.228*** (0.969) −2.582*** (0.568)Constant 41.91*** (8.263) −205.3*** (54.04) 142.5*** (35.39)Observations 63 63 63 63R-squared 0.388 0.624 0.211 0.486


significant impact on the magnitude of itsprice swings during a housing boom.

Comparison with Models Utilizing Saiz-Type Supply Measure

To date, authors wishing to investigate therelationship between housing supply condi-tions and price and construction dynamicsduring boom and bust cycles have reliedheavily upon Saiz’s (2010) measure of theshare of undevelopable land within a metro-politan area to proxy for differences in theelasticity of housing supply. The popularity ofthe Saiz proxy stems from the presumptionthat it is likely exogenous to housing price andquantity changes. However, Cox (2011) de-scribes it as “blunt in the extreme” because itis based on the availability of developableland in areas of the same geographic size (acircle with a 50 km radius) regardless of thesize of the metropolitan area and ignores theland area that is already developed. Our dataset, augmented by the Saiz proxy measured atthe county level in Florida, allows us to in-vestigate the relationship between the proxyand our estimated housing supply elasticities.Additionally, having both the proxy variableand the supply elasticity measure enables usto inspect to what extent the use of a proxyvariable to test the hypotheses of Glaeser,Gyourko, and Saiz (2008) may result in spu-rious inference.

Results Using Saiz Proxy

Tables 8 and 9 report the results obtainedfrom reestimating the price appreciation andnew construction models for the boom andbust periods using the Saiz proxy (in lieu ofour estimated supply elasticities). In the2000–2006 price appreciation models, the es-timated coefficient on the proxy is positiveand statistically significant. In the 2007–2010models, the proxy is negative but significantonly in the bivariate model. These results par-allel those obtained using our estimated sup-ply elasticities (reported in Tables 2 and 3)and lend support to the use of the Saiz proxy.However, a reliable proxy variable for thesupply elasticity must be able to explain bothprice and quantity changes. In the construc-tion models, the Saiz proxy is not significantin any of the four models estimated (see Table9), which lends support to Cox’s criticism thatthe proxy may be too crude for the purposesat hand.

Finally, we estimated elasticity regressionsthat included the Saiz proxy as an explanatoryvariable using three different specifications:(1) a bivariate model including only the Saizproxy, (2) a fully specified model in which theSaiz proxy is used in lieu of our measure ofundeveloped land, and (3) a fully specifiedmodel that included both the Saiz proxy andour measure of undeveloped land. The resultsfrom these models are reported in Table 10.


TABLE 9New Construction Regressions with Saiz-Type Supply Measure, Dependent Variable: ln(New Construction)

Time Frame

Variable 2000–2006 2000–2006 2007–2010 2007–2010(1) (2) (3) (4)

% Undevelopable land 0.119 (0.598) 0.202 (0.755) −0.163 (0.475) 0.0398 (0.598)% College graduates 0.000301 (0.0203) 0.00230 (0.0173)Income per capita 1.64e–05 (3.57e–05) −8.94e–06 (3.83e–05)Income growth −5.42e–06 (6.82e–05) 2.79e–05 (6.77e–05)Rainfall −0.0208 (0.0213) −0.0310* (0.0168)January temperature −0.0269 (0.0289) −0.0305 (0.0207)ln(Total housing units) 1.284*** (0.0638) 1.268*** (0.106) 1.042*** (0.0527) 1.092*** (0.0953)ln(Land area) −0.515* (0.268) −0.425 (0.303) −0.365* (0.216) −0.328 (0.253)Constant −1.884 (1.693) −0.0430 (2.557) −1.423 (1.451) 1.253 (2.229)Observations 63 63 63 63R-squared 0.853 0.859 0.836 0.847


TABLE 10Elasticity Regressions, Dependent Variable: Short-Run Elasticity


Variable Standard Standard Standard Corrected Corrected Corrected(1) (2) (3) (4) (5) (6)

% Undevelopable land −1.628(1.203)

−0.538(1.862)

0.887(1.971)

−1.566(1.185)

−0.473(1.834)

0.934(1.941)

Minimum lot size −1.007***(0.332)

−0.876***(0.321)

−0.984***(0.325)

−0.855***(0.314)

Undeveloped land 2.46e–05***(8.93e-06)

2.43e–05***(8.74e–06)

Planning expenditures −6.97e–08(5.32e–08)

−1.52e–07***(5.61e–08)

−7.05e–08(5.23e–08)

−1.52e–07***(5.53e–08)

Average housing value −1.70e–05*(8.80e–06)

−1.88e–05**(8.06e–06)

−1.67e–05*(8.63e–06)

−1.84e–05**(7.91e–06)

Millage 0.114(0.111)

0.210*(0.111)

0.112(0.109)

0.207*(0.109)

Constant 2.717***(0.584)

2.384(2.303)

−0.714(2.527)

2.655***(0.573)

2.316(2.258)

−0.741(2.478)

Observations 63 63 63 63 63 63R-squared 0.019 0.143 0.220 0.018 0.142 0.221


In none of these models is the Saiz proxy sta-tistically significant. These results, along withthose obtained from using the Saiz proxy toexplain new construction, suggest that re-searchers should be cautious in their relianceon Saiz’s proxy for the supply elasticity.

V. CONCLUSION

Mixed evidence is provided in recent stud-ies that have investigated the role played by

interarea differences in supply elasticity in af-fecting the price and quantity changes that oc-cur during a boom and bust housing cycle. Inthis paper, instead of using proxies for thesupply elasticity, we use 21-year panels to es-timate the short-run price elasticity of housingsupply directly for 63 Florida counties. In ar-eas where the supply elasticity is higher, wefind that boom periods are characterized bygreater construction and less price apprecia-tion. During bust periods, there is more new


construction in high supply-elasticity areas,but the magnitudes of price changes are un-affected by whether the supply elasticity ishigh or low.

The importance of supply elasticity as a de-terminant of price and quantity changes thatoccur in response to demand shocks begs thequestion: What factors account for differencesin the supply elasticity across areas? We pro-vide evidence that suggests that minimum lotsize requirements, local comprehensive plan-ning expenditures, the amount of developableland, and the local government’s fiscal situa-tion all affect the construction industry’s re-sponse to higher prices.

An important unanswered question regard-ing the effect that supply has on housing mar-kets during a boom and bust cycle is the extentto which overbuilding occurs on the upswing.More overbuilding translates into greater ex-cess inventory as the cycle turns, whichcauses greater price deflation in the downturn.The relatively cheap housing units may affecthousehold location decisions, resulting in sig-nificant welfare losses. Future research shouldattempt to develop reliable counterfactuals ofthe level of construction that would have oc-curred in the absence of the boom and bustphenomenon.

Acknowledgments

Keith Ihlanfeldt thanks the Federal Home LoanBank of Atlanta for financial support. Both authorsthank Brad Huff for excellent research assistance onthis project. The views expressed in this paper arethose of the authors and do not necessarily reflectthose of the Office of the Comptroller of the Currencyor the U.S. Department of the Treasury.

References

Bailey, Martin J., Richard F. Muth, and Hugh O.Nourse. 1963. “A Regression Method for RealEstate Price Index Construction.” Journal of theAmerican Statistical Association 58 (304): 933–42.

Banerjee, Anindya, Juan J. Dolado, John W. Gal-braith, and David Hendry. 1993. Co-integration,Error Correction, and the Econometric Analysisof Non-stationary Data. Oxford: Oxford Univer-sity Press.

Cheung, Ron, Keith Ihlanfeldt, and Tom Mayock.2009. “The Incidence of the Land Use RegulatoryTax.” Real Estate Economics 37 (4): 675–704.

Cox, Wendell. 2011. “Constraints on Housing Sup-ply: Natural and Regulatory.” Econ Journal Watch8 (1): 13–27.

Davidoff, Thomas. Forthcoming. “Supply Elasticityand the Housing Cycle of the 2000s.” Real EstateEconomics.

DiPasquale, Denise. 1999. “Why Don’t We KnowMore about Housing Supply?” Journal of Real Es-tate Finance and Economics 18 (1): 9–23.

DiPasquale, Denise, and William C. Wheaton. 1994.“Housing Market Dynamics and the Future ofHousing Prices.” Journal of Urban Economics 35(1): 1–27.

Fischel, William A. 1989. “Do Growth Controls Mat-ter? A Review of Empirical Evidence on the Ef-fectiveness and Efficiency of Local GovernmentLand Use Regulation.” Working Paper 87-9. Cam-bridge: Lincoln Institute of Land Policy.

Gallant, A. Ronald. 1981. “On the Bias in FlexibleFunctional Forms and an Essentially UnbiasedForm: The Fourier Flexible Form.” Journal ofEconometrics 15 (2): 211–45.

Glaeser, Edward L., Joseph Gyourko, and Albert Saiz.2008. “Housing Supply and Housing Bubbles.”Journal of Urban Economics 64 (2): 198–217.

Green, Richard K., Stephen Malpezzi, and StephenK. Mayo. 2005. “Metropolitan-Specific Estimatesof the Price Elasticity of the Supply of Housing,and Their Sources.” American Economic Review95 (2): 334–39.

Grimes, Arthur, and Andrew Aitken. 2010. “HousingSupply, Land Costs and Price Adjustment.” RealEstate Economics 38 (2): 325–53.

Gyourko, Joseph, Albert Saiz, and Anita Summers.2008. “A New Measure of the Local RegulatoryEnvironment for Housing Markets: The WhartonResidential Land Use Regulatory Index.” UrbanStudies 45 (3): 693–729.

Huang, Haifang, and Yao Tang. 2012. “ResidentialLand Use Regulation and the U.S. Housing PriceCycle between 2000 and 2009.” Journal of UrbanEconomics 71 (1): 93–99.

Ihlanfeldt, Keith R. 2009. “Does ComprehensiveLand-Use Planning Improve Cities? Land Eco-nomics 85 (1): 74–86.

Mayer, Christopher J., and C. Tsuriel Somerville.2000. “Residential Construction: Using the UrbanGrowth Model to Estimate Housing Supply.”Journal of Urban Economics 48 (1): 85–109.

Mayock, Tom. 2009. “What Makes a Community(Un)affordable? An Empirical Analysis of the De-


terminants of Affordable Housing Supply.” Un-published manuscript, Florida State University.

McMillen, Daniel P., and Jonathan Dombrow. 2001.“A Flexible Fourier Approach to Repeat SalesPrice Indexes.” Real Estate Economics 29 (2):207–25.

Quigley, John M., and Larry A. Rosenthal. 2005.“The Effects of Land Use Regulation on the Priceof Housing: What Do We Know? What Can WeLearn?” Cityscape 8 (1): 69–137.

Rosenthal, Stuart S. 1999. “Residential Buildings andthe Cost of Construction: New Evidence on theEfficiency of the Housing Market.” Review of Eco-nomics and Statistics 81 (2): 288–302.

Saiz, Albert. 2010. “The Geographic Determinants ofHousing Supply.” Quarterly Journal of Economics125 (3): 1253–96.

Topel, Robert, and Sherwin Rosen. 1988. “HousingInvestment in the United States.” Journal of Po-litical Economy 96 (4): 718–40.

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Housing Bubbles and Busts: The Role of Supply … Bubbles and Busts: The Role of Supply Elasticity Keith Ihlanfeldt and Tom Mayock ABSTRACT. Existing studies of the relationship be-tween