M a r k e t i n g T i m e a n d P r i c i n g S t r a t e g i e s

7/30/2019 M a r k e t i n g T i m e a n d P r i c i n g S t r a t e g i e s

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M a r k e t i n g T i m e a n d P r i c i n g S t r a t e g i e s

Auth o r s Eddie CM. Hui, Joe T.Y. Wong, andK.T. Wong

Abstract This study examines how overpricing of properties (in terms ofabove-market price), along with various housing attributes,influence their time-on-market (TOM). The results with the fullsample show that only dummy variables depicting years 2 0 0 3 -2005 and flats located in Kowloon significantly affect TOM. Inthe sub-period analyses, however, factors such as the above-market price, sale price, a flat's tenure status, general propertyprice trend, and changes in unemployment rate have significantimpacts on TOM, yet their respective impacts change over time.Specifically, the effectiveness of overpricing in optimizingsellers' returns and TOM depends on economic conditions aswell as on the availability of other alternatives on the housingmarket.

In the valuation world, property appraisers all too frequently relate marketing timeto the estimated capital value of a real property. For example, for their definitionof "market value" or "open market value," a "reasonable" time period in themarket is assumed. Aside from various housing characteristics (Hui, Chau, Pun,and Law, 2007), other important elements that can influence the incentive to buyare the sellers' initial asking prices and their expectations of the selling price(Wong, Hu, Seabrooke, and Raftery, 2005). While housing characteristicsgenerally remain static over a long period of time, list prices, consumers'preferences, and price aspirations and expectations may change over time.Although one would argue that the housing attributes may also undergo dramaticchanges, such as urban renewal or redevelopment, it is usually an exception ratherthan a norm.An optimal asking price and optimal time-on-market (TOM) would maximize thenet realizable return from residential sales. From a seller's perspective, onequestion being asked is whether he/she is able to obtain a higher selling priceover a longer period of time, or just settles for a lower selling price over a shorterperiod of time. Thequestion that follows may be: What is an optimal sellingperiod if the property is sold at the current market value? From a buyer'sperspective, he/she would look for the best possible price for a flat, subject to theamount of information and search costs. In other words, how much a seller could


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3 76 H u i , W o n g , a n d W o n g

willing to spend, whether in terms of time or money, on searching for relevantmarket information and similar flats available [for the respective psychology ofbuyers and sellers, see Wong and Hui (2008)]. Such considerations dictate theTOM for a fiat for sale and the seller's realized return above market price. If thebuyer's search costs exceed the seller's potential gain through a higher list price,the transaction is likely to take place sooner, thus a shorter TOM, and vice versa.The latter scenario could reach a point where transactions fail. For instance, A nglin(1994) reported that 30% to 50% of negotiations ended w ithout a transaction w hileAnglin, Rutherford, and Springer (2003) noted that about 40% of house listingsended without a sale.But the main problem remains: Could sellers with higher selling costs achieve afaster sale, and at the same time, a maximum realizable return? Alternatively,could sellers benefit from longer marketing time in terms of achieving greater realselling prices? If not, what is the optimal pricing strategy such that a seller couldachieve the greatest net present value of the selling price in an optimal marketingtime, without prior knowledge of a potential buyer's searching and informationcosts? This study examines the list price, the expected sale price, and the TOMin the property market under different living tenures, flat sizes, price ranges, over(under)-priced properties, and other physical characteristics. This study focuseson Hong Kong's residential market while previous studies have examined theoverseas markets.This paper is organized in four sections. The next section is the literature reviewon the time-price relationship. The sections that follow present the studymethodology, and describe the data source and the empirical results. The lastsection provides concluding remarks.

L i t e r a t u r e R e v i e wPrior to a transaction for real properties, sellers have to decide whether tomaximize selling price or to minimize TOM (Trippi, 1977; Miller, 1978). For theformer, according to K night (2002 ), a seller's search is led by a desire to maxim izethe discounted present value of realized profits from a transaction through a listingprice that balances the marginal costs of continuous searching with the marginalbenefits of accepting an offer at present. In the meantime, a buyer faces a similarproblem, as he/she searches on the housing market for one particular propertythat maximizes his/her utility level (Knight, 2002). Based on such interactionsbetween buyer and seller, a logical progression is how to reach that optimal listprice for the seller. According to Merlo and Ortalo-Mague (2004), it is typically


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M a r k e t i n g T i m e a n d P r i c i n g S t r a t e g i e s 3 7 7

price or an upper bound for the seller's reservation price that will be acceptablewben a prospective buyer matches the offer.Specifically, a seller's choice of an offer price is a function of market price forthe seller's home, its characteristics, and the availability of properties in the market(Mayer, 1995). Subsequently, this list price influences the arrival rate of buyers,as well as the distribution of their bids. However, one reason for sellers to set ahigher list price is that they have a higher loan-to-value ratio, as discovered in aninvestigation of the Boston condominium market by Genesove and Mayer (1997).They also have a higher expected TO M and tend to obtain higher transaction p ricesthan owners with proportionately less debt.Eor the latter, TOM has been studied in three different dimensions. The firstdimension is the relationship between TOM and price concession ratio; the secondis that between TO M and sale price. The last dimension focuses on the how searchtheory explains TOM (Kalra and Chan, 1994). With regard to the first dimension,it has been shown in Belkin, Hempel, and McLeavey (1976), Kang and Gardner(1989), and a more recent study by Leung, Leong, and Chan (2002) that TOMhas a positive correlation with the ratio of list price to sale price. Kalra and Chan(1994) have a similar finding, although the impact of price concession appears tobe more noticeable for the transactions of higher-priced homes. However, it is notalways the case, as Jud, Winkler, and Kissling (1995) report no significant impactof price concession on TOM. Instead, they find that it is influenced by the standarddeviation in sale price. They suggest that a wider distribution of prices mayencourage sellers to hold out for higher transaction prices, therefore resulting ina longer TOM.Meanwhile, some other researchers have concentrated their efforts on examiningthe relationship between T OM and sale price. Yet, the resulting em pirical evidenceon this matter has been mixed and somewhat inconclusive. Specifically, the signon TOM has been found to be rather unpredictable (Asabere, Huffman, andM ehdian, 1993). For instance, som e studies have discovered a positive correlationbetween TOM and sale price (e.g., Trippi, 1977; Miller, 1978; Asabere andHuffman, 1993), whereas an inverse relationsh ip between the two was found inCubbin (1974).Erom a theoretical standpoint, search theory has been frequently used to explainthe relationship between sellers' choice for listing price and TOM. Generallyspeaking, the search theory usually assumes a direct relationship between sellingprice and TOM. Sellers are willing to wait longer for the probability of a betteroffer from a buyer (e.g.. Miller, 1978; Turnbull and Sirmans, 1993; Merlo andOrtalo-Magne, 2004). Yet this may not always be the case, as potential buyersmight regard properties that remain unsold for a relatively long period of time as


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3 7 8 H u i , V v ' o n g , a n d V ^ o n g

numerous occasions in the literature such as Miller (1978), Ong and Koh (2000),Anglin and Wiebe (2004), Li (2004), and Merio and Ortalo-Magne (2004).Asabere, Huffman, and Mehdian (1993) report that both the overpricing andunderpricing strategies would prevent sellers from achieving the optimal TOMsthat correspond to the highest sale prices (in net present values).Besides property prices, housing characteristics are other important determinantsof TOM. Miller (1978) points out that, given a housing supply and demandconditions, a property's TOM is a function of its attractiveness (attributes) incomparison to other properties on the market. A key element of a flat'sattractiveness is its age. However, there has been no consensus about how ageaffects TOM. For instance, Zuehlke (1987) reports that the age of a property isan important determinant of TOM, but Kalra and Chan (1994) and Ong and Koh(2000) reach a different conclusion. On the other hand, Haurin (1988) suggeststhat TOM is directly affected by the atypicality of a house. In other words, ittakes more time for properties with more unusual features to market before theycan be transacted (Kalra and Chan, 1994). For example, Ong and Koh (2000)found that in condominiums in Singapore, those that were on lower floors tendto have longer TOM. Li (2004) found that property characteristics, such as poolview, number of bedrooms or toilet and private enclosed space, are not significantin explaining TOM; however, properties on higher floors that face south or north,and with more convenient parking will have shorter marketing times.Researchers have also studied the impact of (housing or financial) marketconditions on TOM. It has been reported in several studies (e.g., Haurin, 1988;Kalra and Chan, 1994; Yang and Yavas, 1995; Anglin, Rutherford, and Springer,2003) that TOM is influenced by both local and national economic conditions, aswell as being subject to strong seasonal effects. In a study conducted by Leung,Leong, and Chan (2002) in Hong Kong, inflation is another critical factor thatinfluences TOM.

S t u d y M e t h o d o I o g yThis study seeks to provide some empirical insight into the effects of variousfactors on TOM. In particular, we hypothesize that both the above-market priceand the sale price have direct influences on TOM. The precise meaning of 'above-market price' has to be defined carefully, however. Yavas and Yang (1995) pointout that it is potentially problematic to regress TOM on sale price (or likewise,on the ratio of sale price to list price, etc.) because of the simultaneity of the twoquantities. [Belkin, Hempel, and McLeavey (1976) and Kang and Gardner (1987)are susceptible to this problem, although some authors like Cubbin (1974) are


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F , = H'X + s,,.

xpected sale price P^

P = H'X.

is then obtained as the predicted value

(1)

of the sale

(2)

In the second stage, the expected prices obtained from the first stage are used asexplanatory variables (among other variables) to model TOM. Cox's (1972)proportional hazard model has now become well-known. Suppose TOM is acontinuous random variable. Let f(t) denote the probability density functionof TOM, F(t) = Fr(TOM < t) its cumulative distribution function and S(t) =Pr(TOM > i) = 1 - E(t) its survival function, then its hazard rate:

h(t) = f(t)/S(t). (3)

The hazard rate measures the likelihood that a housing unit will be sold at timet, given that it has not been sold at time /. If two houses A and B have hazardrates /i,(0 and h2(t) respectively, with /,() > h2(t) for all t, then house A tendsto have a shorter TOM than house B does. Now, in the Cox model, the hazardfunction of TOM is assumed to take the following form:

h(t) = ho(t)A(X, Z), (4)

where /^o(0^ called the baseline hazard rate, is a nonnegative function of time andA is a time-independent function that may depend on some housing attributes X(age of property, number of bedrooms, whether it is near the central businessdistrict, etc.) some other variables Z that affect both TOM and home prices(months of inventoi^, velocity of sales, P^, P[^, mortgage interest rate,unemployment rate. Consumer Price Index, gross domestic product, etc.).Different definitions of /2o(i) and A give rise to different models of TOM. Eorexample, Jud, Seaks, and Winkler (1996) assume that:


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3 8 0 H u i , V / o n g , o n d V \ / o n g

where the log price difference log(P^/P^) is included in Z as a measure of above-market pricing. /zo(i) = pt^'^ is the baseline hazard rate of the Weibull distribution.When p = 1, the baseline hazard rate becomes constant and the density functionof TOM decays exponentially. H ence Cox m odel reduces to the Exponential modelin this case.Cox models are not the only hazard models that appear in TOM literature. Thesemi-log regression in (3), for instance, can be recast as a hazard model with thefollowing density and survival function:

fit) =


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M a r k e t i n g T i m e a n d P r i c i n g S t r a t e g i e s 3 8

The above-market price is defined as:

A = ln (PJ - E{\n{Ps)). (12)

Regarding (12), note that the formation of A is somewhat similar to the conceptof Degree of Overpricing (DOP) in Anglin, Rutherford, and Springer (2003).However, there are two noticeable differences: (1) the expected transaction/saleprice, instead of expected list price, is being used for the computation of A forthis study; and (2) as stated earlier, the results derived from the hedonic pricingmodel are used for the formulation of E(\V{PS)) in this study, in which onlyhousing attributes (X) are included. Meanwhile, the expected list price (informulation of DOP) in Anglin, Rutherford, and Springer (2003) is subject to bothhousing attributes (X) and market conditions (Z), which differs from conventionalhedonic pricing model specifications. Although there might be issues relating tosimultaneity bias, there is an expected relationship between list price and expectedsale price (unlike expected list price, this is a realized, transaction price that buyersare able to obtain). This somehow drives the negotiation process and determinesTOM. A would be a good measure of the effect upon TOM.The variables in X (housing attributes) are as follows:

ln{SIZE) = The natural logarithm of the size (in square feet)of the housing unit;KLN = A dummy variable that the housing unit for saleis located in the Kowloon Peninsula;NT = A dummy variable that the housing unit for saleis located in the New Territories;E, S, W, N, NE, SE, and AW = Dummy variables that represent the orientationof the housing unit (the direction where the livingroom of an apartment is facing);VACANT = A dummy variable that the housing unit is invacant possession;OWNER = A dumm y variable that the housing unit is owner-occupied;RENTER = A dumm y variable that the housing unit is renter-occupied;FLOOR = A dummy variable; 1 if the flat is higher than the2 0 * floor, 0 otherwise;SPACES = The number of parking spaces entitled to thehousing unit;


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3 8 2 H u i , V ^ o n g , a n d W o n g

The variables in Z (macroeconomic factors) are as follows:RPPI/RPRI = The return (rate of change) of the property price (rental) index,

computed by the Rating and Valuation Department, theHKSAR Government, on a monthly basis;UNEMPLOYCH = The changes in unemployment rate in Hong Kong;CPIYYPERCH = The year-to-year growth of the Consumer Price Index;BLRCH = The changes in the best lending rate; andROCINCOME = The adjustments in median household income.In the second stage, a Cox survival model will be deployed. Both X and Z willbe considered explanatory variables and price-related items such as A or ln(P)will be included in Z. Various researchers (e.g., Zuehlke, 1987; Haurin, 1988;Larsen and Park, 1989; Yang and Yavas, 1995; Ong and Koh, 2000; Genesoveand Mayer, 20 01 ; Ang lin, Rutherford, and Springer, 2003; and Li, 2004) haveincluded different variables X and Z in specifying A. In this study, the proportionalhazard assumption is presumed, as Cox (1972) finds that under such assumption,one can actually use a partial likelihood method to estimate the coefficients B andC in (7) without knowing the baseline hazard function.In the Cox model, if the coefficient of a variable is positive, then an increase inthe value of the variable would result in a longer TOM for flats; otherwise, anincrease in the value of the variable would tend to make TOM shorter.S a m p l e D a t aThe samples were collected on a cross-sectional basis, including their initial askingprices and eventual sale prices, from January 2003 to June 20 06. This was a periodwhen Hong Kong had gradually been recovering from tbe lasting impact of theAsian Financial Crisis in the late 1990s, only to be temporarily hit again due tothe SARS epidemic. The number of properties with negative equity reached ahistoric high of 106,000 by the end of the second quarter of 2003 (Exhibit 1) andunemployment rate was higher than 8% (Exhibit 2). Afterwards, the economyimproved gradually, as seen in the price trend and number of transactions of realproperties in Exhibit 3. The unemployment rate continued to fall and the interestrate had started climbing in 2005 due to higher demand for mortgage loans, asreflected by the increase in property transactions in 2004 and 2005. Unlike inmany western countries. Hong Kong has arguably the largest public housingsystem in the world, along with Singapore. Yet, as a response to the controversiessurrounding the government's assistance homeownership (HOS) programespecially during the economic downturn, the Housing Authority (HA) has ceasedthe construction and sale of HOS flats since 2003. This policy change in some


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E x h i b i t 1 I Number of Residential Negative Equity Cases

Source: Hong Kong Monetary Authority.

The transactions cover pre-sale units (units under construction), owner-occupiedunits, renter-occupied units, and vacant-in-possession units. The data refer todomestic flats randomly selected from 168 development estates and individualbuildings of various flat sizes from less than 272 sq. ft. to over 3,882 sq. ft. Atotal of 62 , 4 3, and 63 estate blocks and individual buildings in Hong Kong Island,Kowloon, and the New Territories are covered, respectively. There are 4,010domestic flat sales records of properties listed as early as January of 2003 andsold as late as June of 200 6. , . . -The data used for this study, unlike many similar studies on Hong Kong's propertymarket, is not from an official source. The reason is that official data such as theEPRC, based on trading records from the Land Registry of the HKSAR, does notnecessarily provide information such as a flat's orientation and its current tenurestatus due to legal concerns. The dataset used for this study, obtained from


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3 8 4 H u i , W o n g , a n d W o n g

E x h i b i t 2 I Hong Kong's Unemploymenf Rate and Best Lending Rate

Year/MonthUnemployment Rate -Best Lending Rate

Sources: Census & Statistics Department and HSBC.

E x h i b i t 3 I Price Trend and Number of Transactions of Real Propertie

YearSale and purchase agreements forresidential prapert iesNew completions of residential f latsVacancy rate (%)

2003 200471,576 100,63026,397 26,036

6.8 6.2Note: The saurce is the Rating and Valuation Department.

2005103,362

17,3216.0

200682,47216,579

5.9


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E x h i b i t 4 I Summary Statistics

TenuresPre-saleVacant in PossessionRenter-occupiedOwner-occupiedUnknown i..' .OverallSale Prices$ l - $ 1 . 9 9 m i l$2 mil-$3.99 mil$4 mil-$5.99 mil$6 mil and overOverallNote:"Excludes cases with missing values.

SampleN o .

1502037

323 ^'1493

740101625 . 1117 ' '4148514007

%

3.750.8

' 8 . 137.2

- 0.210040.6 '27.910.321.2

100

E x h i b i t 5 I Disfribution of Marketing Time (TOM)

SoldNo.N of days

P'Yeor

1 s t Qtr1-90217554.2

2nd Qtr91 -18082020.4

3rd Qtr181-27346111.5

4th Qtr274-3652446.1

2 " ' ' Year366-7302776.9

3'^ Year731-1095320.8

After 3rdYear1096-114110.03

Total

4010100

Note that about half (51.0%) of the transacted properties are vacant. Statistics alsoshow that about 40% of them are in the low end of the market (the most commonflat type in Hong Kong), with a list price below HK$2 million.A closer look at the marketing periods of individual properties provides some


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3 8 6 H u i , W o n g , a n d W o n g

E x h i b i t 6 I Hedonic Regression of Natural Log Sale Price I

VariablesConstantlnS;ZKLNNTBEDROOMSDININGCLUBHOUSEPOOLSPACESWNESENWUNKNOWNTENPRSALERENTEROWNER

Coeff.5 . 4 7 2 " '1.487'"

- 0 . 1 5 4 ' "- 0 . 5 2 7 " *- 0 . 0 2 0 "- 0 . 3 0 7 " *

0 . 1 8 8 " *0 . 1 9 4 ' "0.025*0 . 0 4 8 "0 . 0 4 3 ' "0 . 0 5 3 " *0 . 0 6 9 " '

- 0 . 1 8 8 '0.043*0 . 0 9 0 ' "0 .020 '

Notes:N= 3,998, excluding cases with'Significant at the 5% level.** Significant at the 1 % level."Significant at the 10% level.

S t d . Dev.0.1120.0200.0130.0140.0100.0170.0140.0150.0140.0190.0130.0140.0130.1110.0250.0180.010

missing values. The adjusted

T-value48.76573.542

- 1 1 . 5 4 9- 3 7 . 8 3 6- 1 . 9 69

- 1 8 . 0 4 713.64212.948

1.7822.5613.1503.9215.251

- 1 . 7 0 01.6795.0491.957

R-square = 0.900.

S i g .0.0000.0000.0000.0000.0490.0000.0000.0000.0750.0100.0020.0000.0000.0890.0930.0000.050

effected relatively quickly, despite the fact that there is a wide range of TOMfrom 1 day to 1,141 days.

R e s u l t s a n d F i n d i n g sStage 1 : Hedonic Pricing Regression ModelThe results of the hedonic regression, using the full sample, are shown in Exhibit6. Statistically insignificant variables have already been removed through a


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Exhib i t 7 1 Correlation between Selected Economic Variables between January 2003 and June 2006

RPPIBLRCHUNEMPLOYCHCPIYYPERCHRPRIROCINCOME

RPPi BLRCH

1 -0.0651

Note:* Significant at the 5% level.' 'Signif icant at the 1% level.

UNEMPLOYCH-0.259-0.008

1

CPiYYPERCH

-0.0910.370*

-0.1291

RPRI

0.362*0.197

-0.531**0.316*1

ROCINCOME0.443**0.165

-0.403"0.1590.346*

1DINING, SPACES, CLUBHOUSE, and BEDROOMS) are significant at least at the10% level. In regards to fiat location, those located on Hong Kong Island are morecostly than others in Kowloon (KLN) and the New Territories (NT), primarilydue to its better accessibility to tbe Central Business District. Moreover, tbecorrelation between transaction price and the RENTER variable is the highestamong the four significant variables, which points to a relatively higher transactionprice for a renter-occupied flat than for others that have identical attributes. Thereason is that it generates a higher level of transaction cost for the owner (mostlyin terms of time) in order to release the flat from the current leasing contract. Interms of the impact of property attributes on transaction prices, the hedonic modelsuggests that the presence of a swimming pool (POOL) inside a housing estateproduces the most positive effect on flat prices, followed by clubbouse(CLUBHOUSE) and carpark spaces (SPACES). Also, the negative relationshipbetween both DINING/BEDROOMS and transaction price suggests, albeitindirectly, that the size of the rooms play a role in the final transaction price.Lastly, condominiums with living rooms facing northwest, southeast, west, andnortheast have positive correlations with their resultant transaction prices. Overallspeaking, the selected housing attributes and economic variables explain 90% ofthe variations of the nominal sale price (in natural log).Stage 2: Estimation of TOM with Cox Survival ModelCox Survival Model Results (Eull Sample). Before conducting the Cox survival


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3 8 8 H u i , V V ' o n g , a n d V / o n g

Therefore, in order to avoid the bias stemmed from multicollinearity of variablesin Vector Z, only RFFI and UNEMFLOYCH [which is also included in, forexample, Jud, Seaks, and Winkler (1996)] are included in the Cox regressionmodel. The Cox model results of the full sample are illustrated in Exhibit 8.Interestingly, the findings point out that the only significant variables are thelocation variable KLN and the three YEAR dummy variables (YEAR2003,YEAR2004, and YEAR2005). The negatively significant relationship between KLNand TOM indicates that flats located in Kowloon appeared to be transacted slightlysooner than those in other parts of Hong Kong. Of the three YEAR dummyvariables, YEAR2004 has the most significantly negative correlation with TOM,indicating that transactions in 2004 in general tended to take place relativelysooner than those in other years. On the other hand, as all three YEAR variablesare negative, it means that it took relatively longer for flats to be transacted in2006 (the reference variable).The reason why the level of TOM was comparatively low in 2004 is that the HongKong economy began to recuperate from the adverse effects brought by the SARSepidemic a year before. The unemployment situation was gradually improving.Property price level was escalating, which remarkably helped reduce the amountof negative equity flats in the territory (Exhibit 1). In addition, the mortgage rate,which is directly related to the best lending rate, was at a stable low level in 2004(Exhibit 2). These factors prov ided the conditions for a new dem and for residentialproperties. With decreasing negative equity cases, more flats were available on themarket, which reduced search costs, resulting in faster transactions (i.e., lowerTOM) in general.In the meantime, the insignificance of other independent variables in both vectorsX and Z insinuates that, due to changing economic conditions during the studyperiod, the impacts of these housing attributes and macroeconomic factors mighthave been fluctuating and inconsistent over time (i.e., large standard deviations),to the point where statistically significant estimates are not possible to obtain whenthe full sample is deployed. In order to illustrate such year-specific impacts, foursub-period investigations were conducted (2003, 2004, 2005, and 2006). Unlikein many previous studies, the first two years were regarded as a post-crisis (orrecovering) period when Hong Kong's economy just began to recover from theeffects of the SARS epidemic. In contrast, the next two years were similar to thesituations depicted in other studies, as Hong Kong's economy gradually recovered.Sub-period investigations allow for comparisons of the effects (and adjustments)of different variables on TOM in various years, which could contribute to theexisting literature considering the nature of Hong Kong's economy duringthe period. The respective Cox model results for each sub-period are shown inExhibit 9.


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E x h i b i t 8 I T h e C o x M o d e l R e su lts fo r t h e F u l l S a m p l e

V a r i a b l e sA ' -

HPs][iniPsWKLNNTES ' . .W .' N ' I >NE ...:. ') ' - SE - .NW ^lV A C A N T O W N E R ' . ' -RENTERSPACESBEDROOMS . .1CLUBHOUSE ./.: . POOLRPPIUNEMPLOYCH . 'FLOOR .YEAR2003YEAR2004 . . . 'YEAR2005Notes: T h e n u m b e r a f o b s e r v a t i o n s is 4 , 0 0 9 .7 4 . 1 7 9 .* S i g n i fi c a n t a t t h e 5% l ev e l .* *S i g n i fi c a n t a t t h e 1 % le v e l.** * S i g n i f i c a n t a t t h e 1 0 % l ev e l .

Coef f .- 0 . 1 3 6 *

0 . 3 2 7 * * \ 0.454 : > .

- 0 . 0 1 5- 0 . 0 8 7 * -?.-0.066 . . .-0.076 J.- 0 . 0 1 1 -,.-

0. 0570. 0550. 0150 . 0 3 1

-0.014 . .-0.113-0.089 ' .-0.099 , '0.008 ;0.000

-0.015- 0 . 0 2 4 -'i- ,

0 . 0 0 7 ' - 0 . 1 7 9- 0 . 0 1 4- 0 . 1 9 4 * * *- 0 . 3 5 6 * * *- 0 . 2 1 5 * * *

T h e 2 l o g - l i k e l i h o o d is 5 8 4 7 1 . 4 3 2 .

S.E.0 . 0 7 70 . 1 3 50.591

- 0 . 0 2 00. 0470. 0590. 0720. 0710 . 0 7 40. 0730.0580.0580. 0570. 0870.0880. 1000.0530 . 0 3 30. 0490.0530. 0060. 1310 . 0 3 50. 064

. - 0.0530.051

T h e c h i - s q u a r e is 1


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3 9 0 H u i , V / o n g , a n d W o n g

Exh i b i t 9 I The Cox Model Results for the Sub-period Samples

VariablesAA^In(Ps)[ln{PsWKLNNTESWNNESENWVACANTOWNERRENTER

; SPACESBEDROOMSCLUBHOUSEPOOLRPPI

20062 . 7 9 2 * "

- 1 . 7 6 7 * * *3.468**

- 0 . 1 1 0 * *- 0 . 1 0 2- 0 . 1 3 9- 0 . 0 9 6- 0 . 1 2 1- 0 . 0 1 0

0.360**0.124

- 0 . 0 0 30.0711.378***1.323***1 . 1 3 5 "

- 0 . 1 3 70.0380.076

- 0 . 0 0 3- 0 . 1 2 7 * * *

UNEMPLOYCH 2 . 2 4 2 * * *FLOOR - 0 . 1 5 2 *

S.E.0.2900.4961.5880.0520.1190.1500.1840.1830.1850.1680.1490.1510.1460.4160.4190.4420.1360.0830.1280.1390.0170.3170.089

20051.653

- 0 . 8 4 42.624

- 0 . 0 8 20.1140.1960.0270.1000.3640.1980.2030.1130.2130.5120.4610.5150.052

- 0 . 1 1 4- 0 . 0 7 6- 0 . 0 0 8- 0 . 0 2 9

S.E.*** 0.160*** 0.286** 1.094** 0.036

0.083 0.104

0.1300.128

*** 0.1240.130

" 0.1030.104

** 0.101*** 0.185** 0.187** 0.203

0.092* 0.058

0.0870.093

*** 0.010- 4 . 6 3 8 * * * 0.268- 0 . 1 2 3 ** 0 .0 6 0

2004- 0 . 5 3 7 * * *- 1 . 8 6 1 * * *

2.051*- 0 . 0 6 4 *- 0 . 1 0 0

0.079- 0 . 1 9 9- 0 . 0 8 0- 0 . 1 0 9- 0 . 0 9 5- 0 . 1 2 6

0.035- 0 . 0 7 8- 0 . 6 1 1 * * *- 0 . 4 7 3 * * *- 0 . 3 6 8 * *

0.021- 0 . 0 3 1- 0 . 0 7 3- 0 . 1 1 0

0.130***1.683***0.099

S.E.0.1390.3491.0700.0350.0840.1020.1270.1210.1360.1340.1000.0970.0970.1190.1200.1420.0900.0580.0850.0950.0110.2110.061

2003- 3 . 8 6 0 * * *- 2 . 3 6 9 * * *- 2 . 4 3 4

0.061- 0 . 4 3 2 * * *- 0 . 6 7 8 * * *- 0 . 0 8 5

0.065- 0 . 1 0 4

0.045- 0 . 0 9 8- 0 . 1 2 6- 0 . 3 3 9 * *- 0 . 1 5 9- 0 . 1 4 3- 0 . 5 4 0 * *

0.255*0 .238 ' * *

- 0 . 0 2 00.012

- 0 . 0 8 3 * * *- 0 . 1 4 2

0.045

S.E.0.2590.4371.4840.0500.110 ;0.135 A0.1580.163 ;0.1790.1670.1320.1350.1350.2200.2220.2660.1310.0800.1090.1180.014 :.0.2280.086

Notes: The number of observations for 2006 = 624, 2 log-l ikel ihood = 8,934.525, and chi-square = 363.783. The number of observations for 2005 = 1,345, 2 log-likelihood =19,086.480, and chi-square = 482.157. The number of observations for 2004 = 1,307, 2 log-l ikel ihood = 18 ,40 8.7 44 , and chi-square = 33 4.6 77 . The number of observations for 2003 =733, 2 log-l ikelihood = 9,93 7.397 , and chi-square = 997 .106 . * Significant at the 5% level. ** Significant at the 1% level. i j*** Significant at the 10% level. ^


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M a r k e t i n g T i m e a n d P r i c i n g S t r a t e g i e s 3 9

in the previous section [cf., Leung, Leong, and Chan (2002) presented theopposite results]. As seen in Exhibit 9, a number of housing characteristics aswell as macroeconomic factors are significant at least at the 10% level.Effects of Property Price & Pricing Strategy. Assuming a non-linear relationshipbetween TOM and property price variables, the above-market price {A), sale priceln(P5), and their respective square-terms are included in Z as explanatory variables.Since \n{P^ is highly correlated with ln(5/Z'), the latter is removed from Z.Exhibit 8 gives parameter estimates for the Cox model. As one can see, the above-market prices A and A- are significant in the pooled sample and the four sub-periods, indicating that overpricing does have a non-linear impact on the TOMfor a residential property. Across the sub-periods, the findings further suggest thatTOM was relatively shorter in 2003 and 2004 than in 2005 and 2006. In otherword s, for two p roperties with the same price mark-up, the one in 2003 is expectedto be sold faster than that in 2006.Why were properties able to be transacted sooner in 2003/2004 than in 2005/2006 as the economy gradually improved? Considering the amount of propertieswith negative equities in 2003 and 2004 (Exhibit 1), it was more costly forpotential buyers to obtain better bargains as housing supply was lower. As a result,it was entirely possible for sellers to overprice their flats to obtain additionalpremiums yet able to sell them sooner during these two years. Yet, from 2005onwards, as Hong Kong's economy was gradually recovering, the number ofnegative equity flats drastically decreased (Exhibit 1). This means more flats wereavailable on the secondary property market. Besides, potential buyers were subjectto higher interest rates. This reduced demand for housing. In such circumstance,it was the usual selection of either a quicker turnover at a lower list price, or ahigher list price but with a longer TOM. In summary, the effects of overpricingon TOM change overtime, upon the supply of alternatives on the market and theopportunity cost incurred for potential buyers.Similarly, the impact of sale price on TOM is also non-linear. The coefficient ofsale price (in natural log) is significantly positive and its square-term issignificantly negative, from 2004 to 2006. This means that for more costlyproperties, TOM would increase at a decreasing rate. This finding adds toknowledge in that previous studies only found linear relationships between TOMand sale price (e.g., Cubbin, 1974; Trippi, 1977; Miller, 1978; Asabere andHuffman, 1993).Geographical Location. Geographical location of the housing unit is shown besignificant only in 2003, which suggests that flats in Kowloon and the NewTerritories on average took less time to sell than properties in Hong Kong Island.This contrasts with the result of Ong and Koh (2000), who find that housing unitsnear the central business district tend to have shorter TOMs.


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3 9 2 H u i , W o n g , a n d W o n g

being the control variable. The findings are slightly different from that in Li(2004). An underlying reason is that the listed price of flats with relatively inferiororientations (e.g., less popular ones such as those facing north and west) havebeen already adjusted accordingly in response to the demand.Tenure. Zuehlke (1987) shows that TOM can be influenced by the vacancy of ahousing unit. In this study, we further examine how TOM would be affected byother kinds of occupancies. With the exception of 2003, all three tenures (vacant,owner-occupied, and renter-occupied) are significant at the 1% level relative tothe omitted category (pre-sale flats). Their impacts on TOM were negative in thefirst two periods, yet positive in the last two. This suggests that pre-sold residentialunits (the control variable) on average have the longest TOMs in 2003 and 2004,while having the shortest TOMs in the next two periods. Meanwhile, the impactof owner-occupied, renter-occupied, and vacant fiats are very close.A probable reason for the relatively lower TOM for vacant properties is that,although the number of residential negative equity cases was greatly reduced in2004, the property price level was not high enough for homeowners to obtaindesirable profits from a transaction, considering the expenses involved (e.g.,searching for another flat and/or terminating the leasing contract with renters).Owners of vacant flats, however, were able to take advantage of this situation andsold their flats faster, due to lower transaction costs and the instant availability oftheir flats. This resulted in a rather noticeable drop in vacancy rate of Hong Ko ng'sresidential properties, from 6.8% in 2003 to 6.2% in 2004 (Exhibit 4). In a sense,our findings regarding vacancy on TOM extend Zuehlke's (1987) conclusion.Other Housing Attributes. Similar to the findings in Ong and Koh (2000), thefindings indicate a significantly negative relationship between TOM and thedummy variable ELOOR. However, this only applies to 2005 and 2006. In otherwords, in these two sub-periods, residential properties higher than the 20 * floorof a building were sold faster than those lower. Another factor that has significantimpact on TOM is the amount of carpark space, but only negatively in 2006.Except these two, the other selected housing attributes are shown to have nosignificant effect on TOM. It should be noted tbat the insignificance of the numberof bedrooms on TOM is in line with the findings in Anglin, Rutherford, andSpringer (2003).Macroeconomic Variables. Changes in unemployment rate (UNEMPLOYCH) firstshow a significantly positive relationship with TOM in 2004, then negative in2005, and eventually positive again in 2006. In general, an increase inunemployment rate, reflecting worse economic conditions, tends to lengthen TOM,as potential buyers would become a lot more cautious when it comes to costly


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conditions in the future. This insinuates a higher price (and better return) for theirflats. In other words, the noticeably longer TOM recorded under an improvingeconomy in 2005 is due to the expectation from sellers for even better returns inthe near future.With regard to the correlation between TOM and general property price trend(RFFI), the impacts of RRFI on TOM are significantly negative in 2003, 2005,and 2006, but significantly positive in 2004. The reason is that, with theexpectation of an even higher price for properties in the near future, as reflectedin RPPI, a buyer will purchase a property of his/her choosing as soon as possiblealthough the list price may be higher than the buyer's expected market price. Thisconstitutes faster transactions and generally a lower TOM . The positive correlationbetween TOM and RFFI in 2004 can be attributed to a much larger supply ofsecond-hand properties on the market. The amount of negative equity cases hadrapidly attenuated from more than 100,000 cases in 2003 to only 19,000 cases bythe end of 2004 (Exhibit 1), indicating that sellers were more willing to sell theirflats. Meanwhile, new completions of flats in 2003 and 2004 were more or lessidentical (Exhibit 4). Because of these factors, the search cost incurred for buyersto look for better bargains had significantly reduced. As a result, a longer TOMarises as sellers face higher competition for offers.

C o n c l u s i o nThis study examined how overpricing of properties, along with a variety ofhousing attributes and market conditions, can affect their TOM. The duration ofa listed property on the market is important because it represents the liquidity ofreal assets, as well as the respective trade-offs for buyers and sellers. The studydeploys a new two-stage methodology. In the first stage, the above-market price(overpricing) is measured by the difference between list price and expected saleprice, which the latter is regressed through a hedonic pricing model. Then in thesecond stage, the effects of various factors including above-market price on TOMare tested by a Cox model.Our study utilizes a first-hand database that covers about 4,000 transactions ofresidential units on the Hong Kong property market under different living tenures,flat sizes, price ranges, and other physical characteristics. This is believed to bemore reliable and comprehensive to other secondary data available.The empirical results show that TOM has significant relations only with above-market price, the location variable KLN, and the three YEAR dummy variableswhen the sample is analyzed as a whole. The significance of overpricing (or the


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3 9 4 H u i , W o n g , a n d W o n g

Nonetheless, the sub-period investigations offer new, in-depth insights into thedynamics of TOM and overpricing that are not observable when it is investigatedas a whole. In these investigations, factors such as above-market price, sale price,changes in unemployment rate, general property price trend (RPPI), and anapartment's tenure status are all significant on TOM. These findings extend theexisting literature in two different ways. Firstly, as a response to Asabere,Huffman, and Mehdian (1993), this study elucidates that whether to overprice orunderprice a property mainly lies in the market/economic situations of a specificperiod, as well as the supply/availability of alternatives on the housing market.Secondly, our findings further extend the literature (e.g., Haurin, 1988; Kalra andChan, 1994; Yang and Yavas, 1995; Anglin, Rutherford, and Springer, 2003)which states that TOM itself is directly influenced by local and national economicconditions. Actually, such economic conditions play an indirect, but important,role in the effectiveness of overpricing as a strategy in optimizing TOM as wellthat is particularly so when the economy either is during or starts recovering froma crisis (i.e., 2003 and 2004). People are uncertain about future market situations.To make situations worse than it already is, housing prices are so much lowerthan that when homeowners bought their flats. A transaction during this periodmost likely means losses for them (i.e., negative equities), regardless of theirpricing strategies. These factors provide the conditions for lower supply on thesecondary property market.Worse, the significance of RPPI on TOM, which is another new finding in thisstudy, shows that expectations of a downward property price trend in the nearfuture cause buyers to postpone their buying decision in hopes of even betterprices in the future. It results in fewer transactions on the property market. This,in addition to the already-low supply of housing, incurs higher cost (both financialand time) for buyers to obtain information for better deals than usual. Thus, it iseasier for homeowners to sell their flats sooner in that period, provided the sameamount of overpricing (i.e., a lower TOM). By contrast, as the economy graduallyrecovers (i.e., 2005 and 2006), property prices soars accordingly. More flats areavailable on the secondary property market as the number of negative equity flatssignificantly decreases. Information of better bargains is much easier to obtain. Inother words, the search costs for buyers attenuates. Thus, overpricing as a strategywould no longer be as effective as it was before in optimizing TOM for sellers.Similar arguments can be used to explain the effects of variables, such as RPPI,ELOOR, RENTER, OWNER, and VACANT, on TOM.Similar to above-market price, the significant, non-linear relationship between saleprice (with the exception of 2003) and TOM extends the literature (e.g., Trippi,1 9 7 7 ; M iller, 1978; Asabere and Huffman, 1993; Cubbin, 1974). Th is findingshows that luxury residential properties that generally occupy larger space have


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U.S . has shown the world the impact of various economic crises on the real estatemarket. As the world has become more globalized, a more fluctuating marketenvironment has ensued. In other words, both the stock market and the real estatemarket become increasingly more susceptible to demand shocks, both from withinand from the outside. As a result, future studies should focus on how propertyowners respond to such demand shocks through their pricing strategies (e.g., thepossibility of underpricing to ensure faster turnover) in such market conditions.

A p p e n d i xThe table below compares the samples deployed in another Hong Kong study onTOM (Leung, Leong, and Chan, 2002). It is worth noting that the statistics ofTOM as recorded in Leung, Leong, and Chan (2002) is much higher than that ofour sample. In addition, this study uses a first-hand database provided by one ofthe biggest real property agents in Hong Kong, whereas in Leung, Leong, andChan (2002) a secondary data set was utilized. Furthermore, our study primarilyinvestigates how overpricing, among other housing attributes and macroeconomicvariables, influence TOM in a period which the econom y w as gradually recoveringfrom a low point in 2003 when Hong Kong was under the onslaught of the SARSepidemic. On the other hand, Leung, Leong, and Chan (2002) focused on a periodin which Hong Kong's economy had been more fluctuating.

Sample Per iodSample SizeInformat ion Source% of t ransact ion f rom the second-hand marketDistr ibut ion of Market RimeM e a nM e d i a nStd. Dev.M o x .Min .

Leung et a l . (2002)Jan. 1 9 9 3 - D e c . 1 9 9 91 1 , 6 1 2EPRC100%

3 0 1 . 6 7814 6 0 . 7 33 , 0 0 21

This PaperJan. 2 0 0 3 - J u n e 2 0 0 64 , 0 1 0 ^iMidland Realty96.3%

132.50 17 8 11 5 1 . 2 7 11,141 1V

End notes


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3 9 6 H u i , W o n g , a n d W o n g

The conditions included in market value definitions of the Uniform Standards ofProfessional Appraisal Practice (USPAP) in the U.S. generally fall into three categories.One of them is: the conditions of sale (e.g., exposure in a competitive market for areasonable time prior to sale). (Definitions: USPAP 2005.) Secondly, one of theconditions included in open market value definitions of the Royal Institution of CharteredSurveyors (RICS) Red Book 5* edition in the U.K. assumes that, prior to the date ofvaluation there had been a reasonable period (having regard to the nature of the propertyand the state of the market).

R e f e r e n c e sAnglin, P.M. A Summary of Some Data on Buying and Selling Houses. Working paper.University of Windsor, Canada, 1994.Anglin, P.M., R. Rutherford, and T.M. Springer. The Trade-off between the Selling Priceof Residential Properties and the Time-on-the-Market: The Impact of Price Setting. Journalof Real Estate Finance and Economics, 2003, 26:1, 95-111 .Anglin, P.M. and R. Wiebe. Pricing in an Illiquid Real Estate Market. 2004. http://www.uwindsor.ca/Palutnglin.Asabere, P.K. and RE. Huffman. Price Concessions, Time on Market, and the Actual SalePrice of Homes. Journal of Real Estate Finance and Economics, 1993, 6:2, 167-74.Asabere, P.K., E.E. Huffman, and S. Mehdian. Mispricing and Optimal Time-on-Market.Journal of Real Estate Research, 1993, 8:1, 14 9-56.Belkin, J., D.J. Hempel, and D.W. McLeavey. An Empirical Study of Time on MarketUsing Multidimensional Segmentation of Housing Markets. Journal of t h e American RealEstate and Urban Economics Association, 1976, 4:2, 57 -7 5.Cox, D.R. Regression Models and Life-tables (with discussion). Journal of Royal StatisticalSociety, 1972, B 34, 187-220.Cubbin, J.S. Price, Quality and Selling Time in the Housing Market. Applied Economics,1 9 7 4 , 6:3, 171-87.Gen esove, D. and C.T. Mayer. E quity and Time to S ale in the Real Estate Market. AmericanEconomic Review, 1997, 87:3, 255-69.Glower, M., D.R. Haurin, and PH. Hendershott. Selling Price and Selling Time: The Impactof Seller Motivation. Real Estate Economics, 1998, 26:4, 719-40.Haurin, D.R. The Duration of Marketing Time of Residential Housing. Journal of theAmerican Real Estate and Urban Economics Association, 1988, 16:4, 396-410.Horowttz, J.L. The Role of the List Price in Housing Markets: Theory and an EconometricModel. Journal of Applied Economics, 1992, 7:2, 115-29.H u i , C M ., C .K. Chau, L. Pun, and M.Y. Law. Me asuring the Neighboring andEnvironmental Effects on Residential Property Value: Using Spatial Weighting Matrix.Building and Environment, 2007, 42, 2333-43.


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Kalra, R. and K.C. Chan. Censored Sample Bias, Macroeconomic Factors and Time onMarket of Residential Housing. Journal of Real Estate Research, 1994, 9:2, 253-62.Kang, H.B. and M.J. Gardner. Selling Price and Marketing Time in the Residential RealEstate Market, Journal of Real Estate Research, 1989, 4:1, 21 -35 .Knight, J.R. Listing Price, Time on Market, and Ultimate Selling Price: Causes and Effectsof Listing Price Changes. Real Estate Economics, 2002, 30:2, 213-37.Larsen, J.E. and W.J. Park. Nonuniform Percentage Brokerage Commissions and RealEstate Market Performance. Real Estate Economics, 1989, 17:4, 422-38.Leung, C.K.Y., Y.C.F. Leong, and I.Y.S. Chan. TOM: Why Isn't Price Enough?International Real Estate Review, 2002, 5:1, 91-115.L i , W.F. The Impact of Pricing on Time-on-Market in High-rise Multiple-unit ResidentialDevelopments. Pacific Rim Property Research Journal, 2004, 10:3, 305-27.Mayer, C.J. A Model of Negotiated Sales Applied to Real Estate Auctions. Journal ofUrban Economics, 1995, 38, 1-22.Merlo, A. and F. Ortalo-Magne. Bargaining over Residential Real Estate: Evidence fromEngland. Journal of Urban Economics, 2004, 56:2, 192-216.Miller, N.G. Time on Market and Selling Prices. Journal of t h e American Real Estate andUrban Economics A ssociation, 1978, 6:2, 164-74.Ong, S.E. and Y.C. Koh. Time-on-Market and Price Trade-offs in High-rise Housing Sub-markets. Urban Studies, 2000, 37:11, 2057-71 .Rating and Valuation Department (various years). The Property Review (various issues).Rating and Valuation Department, the Government of Hong Kong Special AdministrativeRegion, PR China.Taylor, C. Time on the Market as a Sign of Quality. Review of Economic ^iwdies, 1994,6 6 : 3 , 555-78 .Trippi, R.R. Estimating the Relationship between Price and Time to Sale for InvestmentProperty. Management Science, 1977, 23:8, 83 8-4 2.Turnbull, G.K. and C.F. Sirmans. Information, Search, and House Prices. Regional Scienceand Urban Economics, 1993, 23:4, 545-57.W ong, J.T.Y., C M . H ui, W. Seabrooke, and J. Raftery. A Study of the Hong Kong PropertyMarket: Housing Price Expectations. Construction Management and Economics, 2005, 23,757-795 .W ong, J.T.Y. and C M . H ui. The M yth of Property Prices: On the Psychology of Sellersand Buyers. Property Management, 2008, 26:3, 171-90.Yang, S.X. and A. Yavas. Bigger is not Better: Brokerage and Time-on-Market. Journal ofReal Estate Research, 1995a, 10:1, 2 3 - 3 3 .Yavas, A. and S.X. Yang. The Strategic Role of Listing Price in Marketing Real Estate:Theory and Evidence. Real Estate Economics, 1995, 23:3, 347-68.Zuehlke, T.W. Duration Dependence in the Housing Market. Review of Economics andStatistics, 1987, 69:4, 701-04.


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generous supply of the transaction data for this study and their insighiful comm entson the performan ce of the property market in Hon g Kong . The team also thanks DrHankel Fung and Mr. Ka-hung Y u for their assistance.

Eddie CM. Hui, Hong Kong Folytechnic University, Hung Hom, Kowloon, Hong Kongor bscmhui @ inet.polyu. edu. hk.


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