Improved Methods for Predicting Property Prices in Hazard Prone Dynamic Markets

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.

Repetitive floods intensify outmigration and climate gentrification in coastal cities

“…The choice of the pricing algorithm with hedonci analysis and kriging is based on rigourous cross-validation of actual property transactions [48] The correction for demand was implemented in the model after consulting with real estate agents in North Carolina in 19× half-hour to one hour interviews structural characteristics of properties (e.g. age, sq.…”

Estimation of Housing Price Variations Using Spatio-Temporal Data

“…In the case of spatial autocorrelation of the error terms, the spatial error model (SEM) is not better than the geostatistical approach [12]. The literature suggests that the kriging geostatistical method improves the prediction performance of spatial hedonic models [13]. Moreover, kriging models are superior to other methods (OLS, SEM, spatial lag model, etc.)…”

An integrated model of real estate market responses to coastal flooding

“…The econometric tradition offers the hedonic pricing model, an established method in real property studies that sticks close to the empirical data and is useful for estimating the typical tradeoffs that economic actors make among quality attributes of goods and environmental qualities at a given point in time, including the loss of value due to flood risk (Atreya, Ferreira, & Kriesel, ; Bin & Polasky, ), the extent to which flood risk is capitalized into property values (Beltrán, Maddison, & Elliott, ), how this evolves over time (Beltrán, Maddison, & Elliott, ), and the presence of neighborhood effects (de Koning, Filatova, & Bin, ). However, the hedonic model does not adequately detect the non‐marginal changes present within the dynamics of the real property market.…”