Shlomie Hazam Daniel Felsenstein Funded by the German-Israel Fund Institute of Urban and Regional Studies, Hebrew University of Jerusalem The Effect of Terror on Behavior in the Jerusalem Housing Market

Descriptive: Terror Patterns Descriptive: 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 clustered? Terror over time: Increasingly random or clustered? Analytic: Modeling Impact of Terror Effect of Terror on House Prices and Rents? Effect of Terror on House Prices and Rents? Do Spatial Spillover Effects Exist? Do Spatial Spillover Effects Exist? Objectives

Theory Terror Generates (1) Risk (2) Fear. [Becker and Rubinstein 2003]. Risk(β) = numerical probability. Not sufficient to change behavior patterns. BUT in combination with FEAR can have great impact on behavior (SARS mad-cow disease): Fear( γ ) = subjective, different threshold, accommodation levels. Y 1t = α+β+ γ+ μ 1t Y 2t = α+β+μ 2t t=1….T Y = observed behavior β = risk γ = fear μ = unexplained factors

Model 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 House Prices Rental Prices

Estimation Model (levels) P i1 = α 1 +β 1 X 1 +μ i1 | [μ i1 = V i + ε i1 ] (levels) P i2 = α 2 +β 2 X 2 +μ i2 | [μ i2 = V i + ε i2 ] (differences) ΔP = α 2 - α 1 + β 2 X 2 - β 1 X 1 + Δ μ V = neighborhood attributes ε = property attributes

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

Terror attacks which took place over the periods (with 1990, 1995 benchmarks) Most of the attacks are located in the vicinity of the “ seam line ”. Note the infiltration of attacks on the west side of the seam line over period

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

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

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

Red zones, the most expensive areas in the city, are located in the west and in the center of Jerusalem. Green zones, the cheaper areas, are located in the vicinity of the seam line and in the peripheral neighborhoods.

In 2004 real estate prices were lower than in the 1999, due to global processes and the high-tech ‘ bust ’. The distribution of the dwelling prices changes mainly in the marginal areas, which became cheaper. The city ’ s center remains expensive.

The difference in dwelling prices between 1999 and 2004 (accounting for the real estate price index). The green areas presents a rise in the prices and the red areas presents a decline.

Descriptive Patterns of Terror (movement of center of gravity, creation of ‘ hot spots ’, increasing randomization) Descriptive Patterns of Terror (movement of center of gravity, creation of ‘ hot spots ’, increasing randomization) Spatial Changes in House / Rental Prices Spatial Changes in House / Rental Prices The Factors that Affected House / Rental Prices The Factors that Affected House / Rental Prices GIS Descriptive Results

The main mass of terror attacks was in the city center. In the next map we calculated the geographic center of terror attacks of each year. The square symbol points in the map, present the geographic center of all of the recorded attacks of a single year, and the triangle symbol points present the weighted mean center of each year. The weighting factor is the number of casualties Weighted Mean uses the following equations to calculate the weighted mean center of a cluster of points : 1. Movement of Center of Gravity

The geographic center of the terror attacks in both cases is in the city center and in the vicinity of the seam line. The movement of the mean points over time is in the general direction of north- south (seam line). The most crowded areas in the city, with the highest number of casualties are not dwelling areas, but the central business district of Jerusalem.

In order to find where were the most intense terror activity in the city in terms of causalities, we used the GIS neighborhood statistics function. This function computes a statistic raster based on the value of the processing cell and the value of the cells within a specified neighborhood. 2.Terror Intensity (Hot Spots)

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

where is the value of i point, is the weight for point i and j for distance d is the weight for point i and j for distance d The G statistic (Getis and Ord 1992) measures concentrations of high or low values for an entire study area random=>fear factor=>consumer behavior=>housing prices 3. Terror Over Time: Clustered or Random?

Observed General G = Expected General G = General G Variance = e-009 Z Score = Standard Deviations

Surface Interpolating Visiting every location in a study area to measure the prices is difficult. Instead, we use the input point locations, and a predicted value can be assigned to all other locations. By interpolating, we predict prices values between these input points. Spatial Changes in House Prices in relation to terror activity

Several interpolation methods were tested – The best results were obtained by: Kriging interpolation - that assumes nearby dwelling price points have similar values and that the distance or direction between sample points shows spatial correlation that helps to describe the surface. (this is the logical price structure of neighborhoods).

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

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

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

The ‘ height ’ in the 3D map is presented by z- values of the difference in dwelling prices between The steep ‘ mountains ’ are the peripheral neighborhoods. The following map shows this result from different angle.

South East View Gilo the old city Talpiot Ramot Armon Ha ’ Natziv

South West View Gilo the old city Talpiot Ramot Armon Ha ’ Natziv

Zonal Overlay Statistics Zonal functions take a value raster as input and calculate for each cell some function or statistic using the attack value for that cell and all cells belonging to the same attack zone. Zonal functions quantify the characteristics of the geometry of the input zones. Correlating price data and terror activity data

STDMEANTERROR TYPECOUNT %shooting %mortar bomb %molotov cocktail %stabbing %grenade %explosive device %attack %arson2 Distribution of average decline in price by terror type of activity

Running a regressionof points of terror (intensity) on prices points derived from the grid, produced non significant explanation with great errors … Using price points from the grid the grid This led us to enlarge the unit of investigation to the statistical zone (i.e. neighborhoods)

analysis OLS 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 autocorrelation Spatial autocorrelation is when the value at one point in space is dependent on values at the surrounding points. That is, the arrangement of values is not just random. Positive spatial correlation means that similar values tend to be near each other. We model spatially dependent data by using ‘ Spatial Lag Model ’ which estimates for an effect of neighboring areas.

Residuals maps of 1999 and 2004 clearly show spatial autocorrelation

Regression Spatial 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?.

Conclusions Descriptive results Most attacks took place in the peripheral neighborhoods. A spatial pattern of terror exists: unarmed attacks and stabbings exists in the vicinity of the seam line, shootings mainly in South (Gilo) and suicide bombing in crowded areas, especially city center. Most attacks took place in the peripheral neighborhoods. A spatial pattern of terror exists: unarmed attacks and stabbings exists in the vicinity of the seam line, shootings mainly in South (Gilo) and suicide bombing in crowded areas, especially city center. Geographic center of gravity for terror events shifted over time towards the seam line. Geographic center of gravity for terror events shifted over time towards the seam line. Neighborhood statistics method emphasized the vulnerability of the city center and creation of ‘ hot spots ’ Neighborhood statistics method emphasized the vulnerability of the city center and creation of ‘ hot spots ’ The G statistic shows that terror became increasingly random over the course of time. This increases the ‘ fear ’ factor. The G statistic shows that terror became increasingly random over the course of time. This increases the ‘ fear ’ factor.

Conclusions Analytical Results Terror has a significant and negative impact on housing prices. Greater significance for rental than purchasing behavior. Shows ‘ fear ’ as main component of terror. More likely to be expressed in short term behavior (rental) than in long term (purchasing). Terror has a significant and negative impact on housing prices. Greater significance for rental than purchasing behavior. Shows ‘ fear ’ as main component of terror. More likely to be expressed in short term behavior (rental) than in long term (purchasing). Population density and housing conditions have a positive and significant affect on housing prices, as expected Population density and housing conditions have a positive and significant affect on housing prices, as expected Significant negative Moran's I coefficient= the impact of terror on housing prices is not ‘ clean ’ – it is also affected by neighboring statistical areas Significant negative Moran's I coefficient= the impact of terror on housing prices is not ‘ clean ’ – it is also affected by neighboring statistical areas Surprising negative and significant spatial lag effect on purchase and rental prices. Perhaps due to the unique, non continuous nature of the Jerusalem housing market? Surprising negative and significant spatial lag effect on purchase and rental prices. Perhaps due to the unique, non continuous nature of the Jerusalem housing market?