Shlomie HazamShlomie HazamDaniel FelsensteinDaniel Felsenstein

Funded by the German-Israel FundInstitute of Urban and Regional

Studies,Hebrew University of Jerusalem

The Effect of Terror onThe Effect of Terror on Behavior in the Jerusalem Behavior in the Jerusalem

Housing MarketHousing Market

Descriptive: Terror PatternsDescriptive: Terror Patterns Has center of gravity moved over time?Has center of gravity moved over time? Can we identify terror ‘Hot Spots’ Can we identify terror ‘Hot Spots’ Terror over time: Increasingly random or Terror over time: Increasingly random or clustered?clustered?

Analytic: Modeling Impact of TerrorAnalytic: Modeling Impact of Terror Effect of Terror on House Prices and Effect of Terror on House Prices and Rents?Rents? Do Spatial Spillover Effects Exist?Do Spatial Spillover Effects Exist?

ObjectivesObjectives

TheoryTheory

Terror Generates (1) Terror Generates (1) Risk Risk (2)(2) Fear Fear. [Becker and . [Becker and Rubinstein 2003].Rubinstein 2003].

Risk(Risk(ββ)) = numerical probability. Not sufficient to = numerical probability. Not sufficient to change behavior patterns. change behavior patterns. BUT in combination with BUT in combination with FEARFEAR can have great impact on behavior (SARS can have great impact on behavior (SARS mad-cow disease):mad-cow disease):

Fear(Fear(γγ) ) = subjective, different threshold, = subjective, different threshold, accommodation levels.accommodation levels.

YY1t1t = = αα++ββ++γγ++μμ1t1t

YY2t2t = = αα++ββ++μμ2t2t

t=1….Tt=1….T

Y= observed behaviorββ = risk = riskγγ = fear = fearμμ = unexplained factors = unexplained factors

ModelModel

Property Characteristics•housing conditions•housing quality

Neighborhood Characteristics•population density•economic level•distance from the seam line

Terror•terror attacks

Macro Effects•interest rates•permanent income

HousePrices

RentalPrices

Estimation ModelEstimation Model

(levels) P(levels) Pi1i1 = = αα11++ββ11XX11++μμi1 i1 | | [[μμi1 = i1 = VVi + i + εεi1i1]]

(levels) P(levels) Pi2i2 = = αα22++ββ22XX22++μμi2 i2 || [[μμi2 = i2 = VVi + i + εεi2i2]]

(differences) (differences) ΔΔP = P = αα2 2 - - αα1 1 + + ββ22XX22 - - ββ11XX11+ + ΔΔ μμ

V = neighborhood attributesε = property attributes

Terror Incident Data – Police DiariesTerror Incident Data – Police Diaries House Prices and Rents – Levi Yitzhak House Prices and Rents – Levi Yitzhak GuideGuide Terror Monetary Damage Data – Property Terror Monetary Damage Data – Property Tax BureauTax Bureau G.I.S. Data AssignmentG.I.S. Data Assignment

DataData

Terror attacks Terror attacks which took place which took place over the periods over the periods 2000-2003 (with 2000-2003 (with 1990, 1995 1990, 1995 benchmarks)benchmarks)

Most of the attacks Most of the attacks are located in the are located in the vicinity of the “seam vicinity of the “seam line”. Note the line”. Note the infiltration of infiltration of attacks on the west attacks on the west side of the seam side of the seam line over period line over period 2002-20032002-2003..

Data Standardization (average price per Data Standardization (average price per meter in $) meter in $) Price Assignment to G.I.S. street/Buildings Price Assignment to G.I.S. street/Buildings cover. cover. Examining Spatial Geographic Weighted Examining Spatial Geographic Weighted Means (hot spots)Means (hot spots)

Data cont.- G.I.S. Data cont.- G.I.S. MethodMethod

Dwelling prices by streetDwelling prices by street. Green color . Green color stands for the cheaper streets, and red color stands for the cheaper streets, and red color stands for the expensive ones.stands for the expensive ones.

The price information was attached to each The price information was attached to each of the of the buildings on every streetbuildings on every street. This . This procedure is necessary for creating price procedure is necessary for creating price surfaces ( to be presented)surfaces ( to be presented)

Red zonesRed zones, the , the most expensive most expensive areas in the city, areas in the city, are located in the are located in the west and in the west and in the center of center of Jerusalem. Jerusalem.

Green zonesGreen zones, , the cheaper the cheaper areas, are areas, are located in the located in the vicinity of the vicinity of the seam line and in seam line and in the peripheral the peripheral neighborhoods.neighborhoods.

In 2004 real estate In 2004 real estate prices were lower prices were lower than in the 1999, due than in the 1999, due to global processes to global processes and the high-tech and the high-tech ‘bust’.‘bust’.

The distribution of The distribution of the dwelling prices the dwelling prices changes mainly in the changes mainly in the marginal areas, which marginal areas, which became cheaper.became cheaper.

The city’s center The city’s center remains expensive. remains expensive.

The difference in The difference in dwelling prices dwelling prices between 1999 and between 1999 and 2004 (accounting for 2004 (accounting for the real estate price the real estate price index). index).

The green areas The green areas presents a rise in the presents a rise in the prices and the red prices and the red areas presents a areas presents a decline.decline.

Descriptive Patterns of Terror (movement Descriptive Patterns of Terror (movement of center of gravity, creation of ‘hot spots’, of center of gravity, creation of ‘hot spots’, increasing randomization)increasing randomization)

Spatial Changes in House / Rental Prices Spatial Changes in House / Rental Prices

The Factors that Affected House / Rental The Factors that Affected House / Rental PricesPrices

GIS Descriptive ResultsGIS Descriptive Results

The main mass of terror attacks was in the The main mass of terror attacks was in the city city centercenter. In the next map we calculated the . In the next map we calculated the geographic center of terror attacks of each year. geographic center of terror attacks of each year.

The square symbol points in the map, present the The square symbol points in the map, present the geographic center of all of the recorded attacks of a geographic center of all of the recorded attacks of a single year, and the triangle symbol points present single year, and the triangle symbol points present the weighted mean center of each year. The the weighted mean center of each year. The weighting factor is the number of casualties weighting factor is the number of casualties

Weighted MeanWeighted Mean uses the following equations to uses the following equations to calculate the weighted mean center of a cluster of calculate the weighted mean center of a cluster of pointspoints : :

1. Movement of Center of 1. Movement of Center of GravityGravity

The geographic The geographic center of the terror center of the terror attacks in both cases attacks in both cases is in the is in the city centercity center and in the vicinity of and in the vicinity of the seam line. the seam line.

The movement of the The movement of the mean points over mean points over time is in the general time is in the general direction of direction of north-north-south (seam line)south (seam line)..

The most crowded The most crowded areas in the city, with areas in the city, with the highest number the highest number of casualties are not of casualties are not dwelling areas, but dwelling areas, but the central business the central business district of Jerusalem. district of Jerusalem.

In order to find where were the most In order to find where were the most intense terror activity in the city intense terror activity in the city in in terms of causalitiesterms of causalities, we used the GIS , we used the GIS neighborhood statisticsneighborhood statistics function. function.

This function computes a statistic This function computes a statistic raster based on the value of the raster based on the value of the processing cell and the value of the processing cell and the value of the cells within a specified neighborhood.cells within a specified neighborhood.

22..Terror Intensity (Hot Spots)Terror Intensity (Hot Spots)

We computed the We computed the sum of the casualties sum of the casualties in the radius of 500m in the radius of 500m from each attack from each attack point. We notice that point. We notice that the city center and the city center and the seam line zone the seam line zone suffered the most: suffered the most: nearly 200-400 nearly 200-400 casualties per square casualties per square km. Other significant km. Other significant areas were the areas were the marginal marginal neighborhoods: Neve neighborhoods: Neve Ya’akov, the French Ya’akov, the French Hill, and Gilo which Hill, and Gilo which suffered up to 100 suffered up to 100 casualties per square casualties per square km.km.

where is the value of i point, where is the value of i point, is the weight for point i and j is the weight for point i and j for distance dfor distance d

The The G G statistic (Getis and Ord 1992) statistic (Getis and Ord 1992) measures concentrations of high or measures concentrations of high or low values for an entire study arealow values for an entire study area

random=>fear factor=>consumer behavior=>housing prices

3 .Terror Over Time: Clustered or Random?

Observed General G = 0.00037921665857368672Expected General G = 0.00033026303185345842General G Variance = 1.4722722834068171e-009Z Score = 1.2758241065266294 Standard Deviations

1990

1995

2000

2001

2002

2003

2001

Surface InterpolatingSurface Interpolating

Visiting every location in a study area to Visiting every location in a study area to measure the prices is difficult. Instead, we measure the prices is difficult. Instead, we use the use the input pointinput point locations, and a locations, and a predicted valuepredicted value can be assigned to all other can be assigned to all other locations. locations.

By interpolating, we By interpolating, we predictpredict prices values prices values between these input points.between these input points.

Spatial Changes in Spatial Changes in House Prices in relation House Prices in relation

to terror activityto terror activity

Several interpolation methods were Several interpolation methods were tested – The tested – The best resultsbest results were obtained were obtained by:by:

KrigingKriging interpolationinterpolation - that assumes - that assumes nearby dwelling price points have nearby dwelling price points have similar valuessimilar values and that the and that the distance or distance or direction between sample points shows direction between sample points shows spatial correlationspatial correlation that helps to describe that helps to describe the surface.the surface.

(this is the logical price structure of (this is the logical price structure of neighborhoods). neighborhoods).

The output The output interpolated grid of interpolated grid of 1999, shows that 1999, shows that there are relatively there are relatively expensive dwelling expensive dwelling areas (colored areas (colored orange/red)orange/red) in the in the some of the marginal some of the marginal neighborhoods.neighborhoods.

The output The output interpolated grid of interpolated grid of 2004, shows that the 2004, shows that the relatively expensive relatively expensive dwelling areas in the dwelling areas in the marginal marginal neighborhoods of neighborhoods of 1999 map 1999 map disappeared and disappeared and now are cheaper. now are cheaper. Other areas in the Other areas in the western city became western city became more expensive.more expensive.

The following map The following map shows the shows the interpolated grid of interpolated grid of the difference in the difference in dwelling prices dwelling prices between 1999-2004, between 1999-2004, over background over background buffers from the seam buffers from the seam line. The terror line. The terror attacks are the black attacks are the black points. The red points. The red zones are the areas zones are the areas where prices were where prices were lower in 2004. These lower in 2004. These are the marginal are the marginal neighborhoods, which neighborhoods, which suffered most of the suffered most of the terror attacks. terror attacks.

The ‘height’ in the The ‘height’ in the 3D map is 3D map is presented by presented by z-z-valuesvalues of the of the difference in difference in dwelling prices dwelling prices between 1999-between 1999-20042004 . .

The steep The steep ‘mountains’ are ‘mountains’ are the peripheral the peripheral neighborhoods. neighborhoods. The following map The following map shows this result shows this result from different from different angle. angle.

South East ViewSouth East View

Gilo

the old city

Talpiot

Ramot

Armon Ha’Nat

ziv

South West ViewSouth West View

Gilo

the old city

Talpiot

Ramot

Armon Ha’Nat

ziv

Zonal Overlay StatisticsZonal Overlay Statistics

Zonal functions Zonal functions take a take a value raster as inputvalue raster as input and and calculatecalculate for each cell for each cell some function or some function or statisticstatistic using the attack valueusing the attack value for for that cell and all cells that cell and all cells belonging to the same belonging to the same attack zone. Zonal attack zone. Zonal functions quantify the functions quantify the characteristics of the characteristics of the geometry of the input geometry of the input zones.zones.

Correlating price Correlating price data and terror data and terror activity dataactivity data

COUNTTERROR TYPEMEANSTD

45shooting-25.71%7.17

8mortar bomb-25.65%2.2

10molotov cocktail-25.45%6.39

69stabbing-22.06%6.87

19grenade-21.78%5.64

98explosive device-21.41%7.44

36attack-21.11%8.14

2arson-15.32%8.32

terror type - % mean delta price

shoo

ting,

-25

.71

%

mor

tar b

omb,

-2

5.6

5%

mol

otov

coc

ktai

l, -2

5.4

5%

stab

bing

, -2

2.0

6%

gren

ade,

-21

.78

%

expl

osiv

e de

vice

, -

21.4

1%

atta

ck, -

21.1

1%

arso

n, -

15.3

2%

-30.00%

-25.00%

-20.00%

-15.00%

-10.00%

-5.00%

0.00%

MEANm

ean

% d

elta

Distribution of Distribution of average decline in average decline in price by terror type price by terror type of activityof activity

Running a regressionof Running a regressionof points of terrorpoints of terror (intensity) on (intensity) on prices pointsprices points derived derived from the grid, produced from the grid, produced non significant non significant explanation with great errors…explanation with great errors…

Using price Using price points frompoints from

the gridthe grid

This led us to enlarge the unit of This led us to enlarge the unit of investigation to the statistical zone investigation to the statistical zone (i.e. neighborhoods)(i.e. neighborhoods)

analysisanalysis OLSOLS

•Terror has a negative affect on housing prices. Larger and more significant for rental prices than purchase prices.

•Terror intensity (measured by casualties and damage) had a lager, significant and positive impact on housing prices in 2004 than in 1999, contrary to our expectations.

•The Lagrange tests implies spatial autocorrelation , therefore we should run spatial lag regressions

•Distance to the seam line had a negative affect on prices, but insignificant.•The variables population density and housing conditions had a positive and significant affect on housing prices, as expected.

Spatial autocorrelationSpatial autocorrelation

Spatial autocorrelationSpatial autocorrelation is when the is when the valuevalue at one point in space is at one point in space is dependentdependent on on values values at the surroundingat the surrounding points. That is, the points. That is, the arrangement of values is not just random. arrangement of values is not just random. Positive spatial correlation means that Positive spatial correlation means that similar values tend to be near each other. similar values tend to be near each other.

We model spatially dependent data by using We model spatially dependent data by using ‘‘Spatial Lag ModelSpatial Lag Model’’ which estimates for an which estimates for an effect of neighboring areas.effect of neighboring areas.

Residuals maps of 1999 and 2004 clearly show spatial autocorrelation

RegressionRegression Spatial LagSpatial Lag

•The new dependent variable is the housing prices level in neighbor statistical areas.

•.Housing prices are negatively affected by terror. Rental prices were more significantly affected as in the OLS model.

•Significant negative lag effect – neighboring prices lower prices in the statistical area. Due to the unique, non continuous nature of Jerusalem housing market?.

ConclusionsConclusionsDescriptive resultsDescriptive results

Most attacks took place in the peripheral neighborhoods. Most attacks took place in the peripheral neighborhoods. A A spatial pattern of terror exists:spatial pattern of terror exists: unarmed attacks and unarmed attacks and stabbings exists in the vicinity of the seam line, shootings stabbings exists in the vicinity of the seam line, shootings mainly in South (Gilo) and suicide bombing in crowded mainly in South (Gilo) and suicide bombing in crowded areas, especially city center. areas, especially city center.

Geographic center of gravityGeographic center of gravity for terror events shifted for terror events shifted over time towards the seam line.over time towards the seam line.

Neighborhood statistics method emphasized the Neighborhood statistics method emphasized the vulnerability of the city center and creation of ‘vulnerability of the city center and creation of ‘hot spotshot spots’’

The G statistic shows that terror became The G statistic shows that terror became increasingly increasingly randomrandom over the course of time. This increases the ‘fear’ over the course of time. This increases the ‘fear’ factor.factor.

ConclusionsConclusionsAnalytical ResultsAnalytical Results

Terror Terror has a significant and negative impact on has a significant and negative impact on housing prices. Greater significance for rental housing prices. Greater significance for rental than purchasing behavior. Shows ‘fear’ as main than purchasing behavior. Shows ‘fear’ as main component of terror. More likely to be expressed component of terror. More likely to be expressed in short term behavior (rental) than in long term in short term behavior (rental) than in long term (purchasing).(purchasing).

Population density and housing conditionsPopulation density and housing conditions have a have a positive and significant affect on housing prices, positive and significant affect on housing prices, as expectedas expected

Significant negative Moran's I coefficientSignificant negative Moran's I coefficient= the = the impact of terror on housing prices is not ‘clean’ – impact of terror on housing prices is not ‘clean’ – it is also affected by neighboring statistical areas it is also affected by neighboring statistical areas

Surprising negative and significant spatial lag Surprising negative and significant spatial lag effecteffect on purchase and rental prices. Perhaps due on purchase and rental prices. Perhaps due to the unique, non continuous nature of the to the unique, non continuous nature of the Jerusalem housing market? Jerusalem housing market?