# RECORD fb704039a7cb48f523fc8649b0477ed96bedc7f7
# TITLE: Improved Methods for Predicting Property Prices in Hazard Prone Dynamic Markets
# YEAR: 2016.0  VENUE: Environmental and Resource Economics  DOI: 10.1007/s10640-016-0076-5
# AUTHORS: K. de Koning; T. Filatova; Okmyung Bin
# PAGES: 17  CANDIDATE TABLE PAGES (1-based): [5]
# ABSTRACT: Property prices are affected by changing market conditions, incomes and preferences of people. Price trends in natural hazard zones may shift significantly and abruptly after a disaster signalling structural systemic changes in property markets. It challenges accurate market assessments of property prices and capital at risk after major disasters. A rigorous prediction of property prices in this case should ideally be done based only on the most recent sales, which are likely to form a rather small dataset. Hedonic analysis has been long used to understand how various factors contribute to the housing price formation. Yet, the robustness of its assessment is undermined when the analysis needs to be performed on relatively small samples. The purpose of this study is to suggest a model that can be widely applicable and quickly calibrated in a changing environment. We systematically study four statistical models: starting from a typical standard hedonic function and gradually changing its functional specification by reducing the hedonic analysis to some basic property characteristics and applying kriging to control for neighbourhood effects. Across different sample sizes we find that the latter performs consistently better in the out-of-sample predictions than other traditional price prediction methods. We present the specific improvements to the traditional spatial hedonic model that enhance the model’s prediction accuracy. The improved model can be used to monitor price changes in risk-prone areas, accounting for changes in flood risk and at the same time controlling for autonomous market responses to flood risk.


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Environ Resource Econ (2018) 69:247–263
https://doi.org/10.1007/s10640-016-0076-5
Improved Methods for Predicting Property Prices in
Hazard Prone Dynamic Markets
Koen de Koning1
· Tatiana Filatova1 ·
Okmyung Bin2
Accepted: 15 October 2016 / Published online: 26 October 2016
© The Author(s) 2016. This article is published with open access at Springerlink.com
Abstract Property prices are affected by changing market conditions, incomes and prefer-
ences of people. Price trends in natural hazard zones may shift significantly and abruptly after
a disaster signalling structural systemic changes in property markets. It challenges accurate
market assessments of property prices and capital at risk after major disasters. A rigorous
prediction of property prices in this case should ideally be done based only on the most recent
sales, which are likely to form a rather small dataset. Hedonic analysis has been long used
to understand how various factors contribute to the housing price formation. Yet, the robust-
ness of its assessment is undermined when the analysis needs to be performed on relatively
small samples. The purpose of this study is to suggest a model that can be widely applicable
and quickly calibrated in a changing environment. We systematically study four statistical
models: starting from a typical standard hedonic function and gradually changing its func-
tional specification by reducing the hedonic analysis to some basic property characteristics
and applying kriging to control for neighbourhood effects. Across different sample sizes we
find that the latter performs consistently better in the out-of-sample predictions than other
traditional price prediction methods. We present the specific improvements to the traditional
spatial hedonic model that enhance the model’s prediction accuracy. The improved model
can be used to monitor price changes in risk-prone areas, accounting for changes in flood
risk and at the same time controlling for autonomous market responses to flood risk.
B Koen de Koning
k.dekoning@utwente.nl
Tatiana Filatova
t.filatova@utwente.nl
Okmyung Bin
BINO@ecu.edu
1
Department of Governance and Technology for Sustainability (CSTM), University of Twente,
P.O. Box 217, 7500AE Enschede, The Netherlands
2
DepartmentofEconomics,CenterforNaturalHazardsResearch,EastCarolinaUniversity,Greenville,
NC 27858, USA
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Improved Methods for Predicting Property Prices in Hazard…
251
Table 1 Summary statistics of
the property attributes
Variables
Summary (N = 4779)
Mean
SD
Sales price
USD 156,612
87,354
Age of the house
22.4years
19
Number of bedrooms
3.2
0.59
Total structure square feet
2391
993
Lot size in acres
0.64
2.4
Gas heating (= 1)
0.35
0.48
Fireplace (= 1)
0.77
0.42
Face brick (= 1)
0.48
0.50
Hard wood flood (= 1)
0.25
0.43
Good quality (= 1)
0.031
0.17
Vacant home (= 1)
0.0048
0.069
Distance to creek
854 feet
596
Distance to airport
33,966 feet
17,859
Distance to major road
135 feet
99
Distance to business centre
4632 feet
2452
Distance to railroad
5498 feet
6378
Distance to Tar River
20,999 feet
17,587
Distance to park
7490 feet
7051
Sold between Fran and Floyd (= 1)
0.34
0.48
Sold after Floyd (= 1)
0.37
0.47
Floodplain (= 1)
0.064
0.24
The area provides an excellent natural experiment setting for this study in that it had enjoyed
a period of relative calm, not experiencing major hurricane flooding since Hurricane Hazel in
1954, followed by two major hurricanes. Hurricane Fran (1996) produced millions of dollars
in property damages resulting from profuse rainfall, flash floods, and severe storm surge.
Three years later Hurricane Floyd (1999) brought torrential rains and record flooding which
resulted in one of the largest peacetime evacuations in U.S. history (Bin and Polasky 2004).
The data include information on sales price, property characteristics such as age and size,
dummy variables that represent the presence or absence of extra facilities of the property,
spatial information on the distance to amenities and disamenities, flood zoning, and time
of sales. The summary statistics of all the relevant property characteristics can be found in
Table 1.
2.2 Model Specifications
The hedonic price function of a property is given by
Footnote 1 continued
in spatial error models are constructed based on the assumptions of the user, whereas in kriging they are based
on the spatial structure of the error, which is defined in the construction of the semivariogram. The spatial
error model is particularly relevant for proper estimation of the coefficients in the hedonic price estimation,
whereas kriging focuses on the prediction of the dependent variable.
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252
K. de Koning et al.
Table 2 Input variables and their functional forms for the hedonic models
Variables
M1
M2
M3
M4
Age of the house
X + X2
X +
√
X
X
X
Number of bedrooms
X + X2
X +
√
X
X
X
Lot size in acres
X + X2
X +
√
X
√
X
√
X
Total structure square feet
X + X2
X + ln(X)
ln(X)
ln(X)
Gas heating (=1)
X
X
Fireplace (=1)
X
X
Face brick (=1)
X
X
Hard wood flood (=1)
X
X
Good quality (=1)
X
X
Vacant home (=1)
X
X
Log of distance to creek
X
X
Kriging
Log of distance to airport
X
X
Kriging
Log of distance to major road
X
X
Kriging
Log of distance to business centre
X
X
Kriging
Log of distance to railroad
X
X
Kriging
Log of distance to Tar River
X
X
Kriging
Log of distance to park
X
X
Kriging
Sold between Fran and Floyd (=1)
X
X
Sold after Floyd (=1)
X
X
Floodplain (=1)
X
X
X
X
Floodplain × sold btw Fran and Floyd
X
X
Floodplain × sold after Floyd
X
X
A variable ‘Schools’ has not been included in the analysis since the school quality in this particular areas
is rather homogeneous. Furthermore, school rating does not have statistically significant effect on property
prices
ln Pi = β0 +
K

k=1
βkxi
k + Ei
(2)
where ln Pi is the natural log of the sales price of property i, β0 is the intercept, βk is the
coefficient for each property characteristic k, xi
k is the value of characteristic k of property i,
and Ei is the residual of the predicted property price. Our objective is to identify the model that
provides the smallest prediction errors in the out-of-sample predictions for various sample
sizes. The models to be compared include:
– A spatial hedonic model from Bin and Landry (2013) (M1)
– An adjusted version of M1 with different functional forms (M2)
– M2 with a reduced number of input variables (M3)
– M3 whereby spatial variability in property prices is predicted with kriging (M4)
The hedonic analysis is used to estimate the coefficients of the input variables βk (Eq.
2). In M4 hedonic analysis is used to understand the influence of the core spatial variable
of interest (flood risk) on property prices while the rest of the spatial variability in prices
is captured by interpolating the residuals (Ei, Eq. 2) using kriging. A list of all the input
variables can be found in Table 2. M1 is same model with the same specifications as used
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