Download Terror and House Prices

The Effect of Terror on Behavior
in the Jerusalem Housing Market
Daniel Felsenstein
Shlomie Hazam
Funded by the German-Israel Fund (GIF)
Institute of Urban and Regional Studies,
Hebrew University of Jerusalem
Objectives
Descriptive: Terror Patterns
 G.I.S. depiction of Terror Incidence
 Identification of Terror Hot Spots and
Intensity
Analytic: Modeling Impact of Terror
 Effect of Terror on House Prices and Rents
 Spatial Spillover Effects
Theory
Terror Generates (1) Risk (2) Fear. [Becker and Rubinstein 2003].
Risk(β) = probabilities very small - BUT
small probabilities events can have great impact. (SARS mad – cow
disease) DUE TO:
Fear(γ) = subjective, different threshold, accommodation levels.
Y= observed behavior
Y1t = α+β+γ+μ1t
Y2t = α+β+μ2t
t=1….T
β = risk
γ = fear
μ = unexplained factors
Empirical Evidence
Effect of Terror:
Behavior with
respect to:
Public Transport
Demand Side
Supply Side
Passengers
20% decrease
[High Frequency: no change]
[Low Frequency : great change]
Drivers
No differences in
compensation
No greater
likelihood to quit
Security Guard
Employment
10% increase in demand
[likelihood to enter employment]
No change in
compensation
Coffee Shops
Negative Effect
[High Frequency: no change]
[Low Frequency : great change]
------
Long Term: small effect (purchasers)
Short Term: significant effect (renters)
------
Housing Market
Macro Effects
•interest rates
•permanent income
Property Characteristics
•housing conditions
•housing quality
Neighborhood Characteristics
•population density
•economic level
•distance from the seam line
Terror
•terror attacks
Model
House
Prices
Rental
Prices
Model Estimation
(levels) Pi1 = α1+β1X1+μi1
| [μi1 = Vi + εi1]
(levels) Pi2 = α2+β2X2+μi2
| [μi2 = Vi + εi2]
(differences) ΔP = α2 - α1 + β2X2 - β1X1+ Δ μ
V = neighborhood attributes
ε = property attributes
Data
Terror Incident Data – Police Diaries
 House Prices and Rents – Levi Yitzhak Guide
 Terror Monetary Damage Data – Property Tax Bureau


G.I.S. Data Assignment
This map presents the
terror attacks which took
place during the years
2002-2003 (and two bench
marks 1990, 1995). Most
of the attacks are located
in the vicinity of the
“seam line”. We notice an
infiltration of attacks on
the west side of the seam
line during 2002-2003.
Total Terror Casualties by Years and Type of Attack
Type of Attack
1990
1995
2000
2001
2002
2003
Casualties
0
0
0
0
0
0
0
Attacks
0
0
0
1
1
0
2
Casualties
7
3
6
6
2
0
24
Attacks
19
5
3
5
4
0
36
Casualties
16
111
12
435
614
306
1494
Attacks
24
1
3
34
30
6
98
7
0
0
0
0
0
7
14
0
4
1
0
0
19
Casualties
0
0
28
0
0
0
28
Attacks
0
0
7
1
2
0
10
Casualties
0
0
0
1
0
0
1
Attacks
0
0
0
6
2
0
8
Casualties
0
1
4
75
75
4
159
Attacks
1
1
6
24
11
2
45
Casualties
24
10
11
8
3
2
58
Attacks
29
14
10
9
5
2
69
Total Casualties
54
125
61
525
694
312
1771
Total Attacks
87
21
33
81
55
10
287
arson
attack
explosive device
grenade
Data
Casualties
Attacks
molotov cocktail
mortar bomb
shooting
stabbing
Total
G.I.S. Method
Data Standardization (Size and Money Values)
Price Assignment to G.I.S. street/Buildings cover.
 Spatial Geographic Weighted Means
 Delta– House Prices + Rentals 1999-2004

This map clearly expresses the dwelling prices in each of
the streets. The green color stands for the cheaper streets,
and the red color stands for the expensive ones.
The price information was attached to each of the
buildings on every street. This procedure is significant for
creating price surfaces which will be presented later on.
street
The red zones, the
most expensive areas
in the city, are located
in the west and in the
center of Jerusalem.
The green zones, the
cheaper areas, are
located in the vicinity
of the seam line and
in the marginal
neighborhoods of
Jerusalem.
In the year 2004 the real
estate price level in
Israel was lower than in
the year 1999, due to
global processes and the
burst of the high tech
bubble. The distribution
of the dwelling prices
changes mainly in the
marginal areas, which
became cheaper. The
city’s center remains
expensive.
This map shows the
difference in dwelling
prices between 1999 and
2004 (the real estate price
index was taken into an
account). The green areas
presents a rise in the prices
and the red areas presents a
decline.
Findings
Descriptive Patterns of Terror
 Spatial Distribution of Changes in House + Rental Prices
 Factors Affecting House + Rental Prices

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 :
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.
The most crowded areas in
the city, with the highest
number of casualties are
not dwelling areas, but the
central business district of
Jerusalem.
Zooming in to the south
part of the city shows us the
difference in dwelling prices
more clearly: the prices in
marginal neighborhoods
in 2004 were much lower
than in 1999 (abu-Tor,
Talpiot, Arnona, Armon
Ha’Natziv). In the western
city (Rasko, Katamonim,
pat) prices were higher in
2004 than in 1999. The
margin areas were those
who suffered many of the
terror attacks.
Terror Intensity
we used the neighborhood statistics GIS function.
This function computes an output raster where
the value at each location is a function of the
input cells in some specified neighborhood of the
location. For each cell in the input raster, the
Neighborhood Statistics function computes a
statistic based on the value of the processing cell
and the value of the cells within a specified
neighborhood, then sends this value to the
corresponding cell location on the output raster.
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 200-400 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.
Terror Over Time: Clustered or Random?
The G statistics were developed by Getis and
Ord (1992). This measures concentrations of
high or low values for an entire study area
where is the value of i point,
is the weight for point i and j for distance d
1990
2001
1995
2000
2001
2002
Observed General G = 0.00037921665857368672
Expected General G = 0.00033026303185345842
General G Variance = 1.4722722834068171e-009
Z Score = 1.2758241065266294 Standard Deviations
2003
Spatial Descriptive of
Changes in House Prices
Surface Interpolating
 Kriging Interpolation Method
 Statistic areas Distributions

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, the prices values between
these input points will be predicted.
Several interpolation methods were tested – The
best results were obtained by Kriging
interpolation, that assume the distance or direction
between sample points shows spatial correlation
that helps describe the surface.
The assumption is that nearby dwelling price points
has similar values, therefore we proceeded with
increment of the model radius until the surface had
homogeneous levels of distribution of prices .
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 1999-2004, 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
2004. These are the marginal
neighborhoods, which
suffered most of the terror
attacks.
Even though the contours allow us to visualize flat
and steep areas It might be a bit difficult to notice
the prices ‘valley’ and perceive the difference in
‘height’ between the ‘valley’ floor and a ‘ridge’.
Viewing data in three dimensions gives us new
perspectives. 3D viewing can provide insights that
would not be readily apparent from a planimetric
map of the same data.
The ‘height’ in the 3D
map is presented by zvalues of the difference
in dwelling prices
between 1999-2004 . The
high steep ‘mountains’
are the marginal
neighborhoods. The next
following maps shows
this same map from
different angles.
South West View
Ramot
the old
city
Talpiot
Armon
Ha’Natziv
Gilo
Zonal Overlay Statistics
Zonal functions take a value
raster as input and calculate
for each cell some function
or statistic using the value for
that cell and all cells
belonging to the same zone.
Zonal functions quantify the
characteristics of the
geometry of the input zones.
MEAN
STD
45
shooting
-25.71%
7.17
8
mortar bomb
-25.65%
2.2
10
molotov cocktail
-25.45%
6.39
69
stabbing
-22.06%
6.87
19
grenade
-21.78%
5.64
98
explosive device
-21.41%
7.44
36
attack
-21.11%
8.14
2
arson
-15.32%
8.32
terror type - % mean delta price
0.00%
MEAN
-30.00%
arson, -15.32%
attack, -21.11%
-25.00%
explosive device, 21.41%
-20.00%
grenade, -21.78%
-15.00%
stabbing, -22.06%
-10.00%
molotov cocktail, -25.45%
mean % delta
-5.00%
mortar bomb, -25.65%
TERROR TYPE
shooting, -25.71%
COUNT
St.area_#
Terror attacks
casualties
Average delta per st.area
Neighborhood
721
3
0
-1219
Shuaafat
824
3
0
-1064
Armon Hanatziv
822
3
1
-1064
Armon Hanatziv
831
1
0
-926.25
Armon Hanatziv
833
1
1
-854.2
Armon Hanatziv
852
3
1
-808
Gilo
641
8
6
-796
Old City
642
5
13
-796
Old City
532
6
6
-794.4932
Abu Tor
834
2
1
-788.3455
Armon Hanatziv
544
2
4
-756.1882
Arnona
313
1
52
-755.4299
Qiryat Menachem
545
3
5
-716.6129
Talpiot
543
2
0
-706.5984
Talpiot
771
11
77
-641.3303
French Hill
772
2
1
-614.5333
French Hill
844
11
14
-493.5
793
16
9
-304
843
12
13
-298.5932
Gilo
city center
Gilo
St.area_#
Terror attacks
casualties
Average delta per st.area
Neighborhood
322
0
0
146.5211
332
0
0
4.4938
422
0
0
-27.5366
Rasko
511
0
0
-70.9561
Northern Qatamons
124
0
0
-112.5625
Maalot Dafna
512
0
0
-162.7882
Northern Qatamons
211
0
0
-204.641
Qiriyat Tzanz
423
0
0
-222.5075
Rasko
132
0
0
-243
176
0
0
-245.7108
Bucharim
411
0
0
-252.6939
Gonen
412
0
0
-255.9195
San Martin
212
0
0
-274.75
417
0
0
-280.2515
Gonen
414
0
0
-296.8101
Northern Qatamons
213
0
0
-302.1
163
0
0
-307.0426
Zichron Yosef
128
0
0
-311.2642
Meah Shearim
123
0
0
-318.1654
Bucharim
Qiriyat Yovel
Ir Ganim
Nachalat Shivaa
Romema
Romema
Regression Purchase Prices
1999 avg.
price per m
(log)
2004 avg.
price per m
(log)
delta price %
(constant)
1.866*
3.121**
-4.146**
terror attacks (log)
-0.160
-0.008
-0.681
distance from the seam line (log)
-0.529
-1.081
-0.859
population density per built sq. km
4.338**
4.911**
3.001**
housing conditions (sq. m of dwelling per
person (log))
1.656*
1.712*
3.705**
housing quality (% ‘dilapidated’ buildings)
-2.460*
-4.548**
-0.247
0.15
0.25
0.10
Variable (average per st.area)
adjusted R sq
Regression Rental Prices
1999 avg.
price per m
(log)
2004 avg.
price per m
(log)
delta price %
0.398
1.576
-0.129
-4.048**
-3.581**
-0.771
distance from the seam line (log)
-0.400
-0.656
-0.679
population density per built sq. km
4.100**
3.139**
2.559**
housing conditions (sq. m of dwelling per
person (log))
3.266**
3.767**
3.260**
0.19
0.18
0.07
Variable (average per st.area)
(constant)
terror attacks (log)
adjusted R sq
Conclusions



Terror intensity – increases over time
Terror patterns – increasingly random
House + rental prices – largest declines in
peripheral neighborhoods and adjacent to seam
line



Differences between purchasing + rental
behavior.
Terror is significant in rental decisions, less so in
purchasing.
Explanation: if fear is main component of
terror ( and not risk), more likely to be
expressed in short term behavior (rental) than in
long term (purchasing).