A spatio-temporal model of housing prices based on individual sales transactions over time

Abstract

A spatio-temporal model of housing price trends is developed that focuses on individual housing sales over time. The model allows for both the spatio-temporal lag effects of previous sales in the vicinity of each housing sale, and for general autocorrelation effects over time. A key feature of this model is the recognition of the unequal spacing between individual housing sales over time. Hence the residuals are modeled as a first-order autoregressive process with unequally spaced events. The maximum-likelihood estimation of this model is developed in detail, and tested in terms of simulations based on selected data. In addition, the model is applied to a small data set in the Philadelphia area.Key Words: Housing Prices, Spatio-temporal models, Autocorrelation  We owe special thanks to Gloria Guard (President) and Stephanie Wall (Outcomes Manager) at the Peoples' Emergency Center in Philadelphia for providing us with their data. We are also indebted to the referees of an earlier draft of this paper for many helpful comments.

Impact of quality-led design on real estate value: a spatiotemporal analysis of city centre apartments

“…Their model further divided the spatial weight matrix into building and neighbourhood, respectively; and applied the same spatiotemporal filtering, resulting in a model that could suffer from over-parameterisation and multicollinearity issues in small to medium data-sets. This issue was further addressed by Smith and Wu (2009) who proposed a unit by unit (also known as Hadamard) multiplication of the weight matrices. As the data are ordered temporally, time dependence could be restricted so that prices can be influenced only through past transactions.…”

“…In this context, the main empirical advances are observed in hedonic modelling of property markets as the most appropriate tool for analysing implicit prices of composite goods based on the theoretical framework developed by Lancaster (1966) and Rosen (1974). In the existing body of knowledge only a small number of studies has focused on the modelling techniques that reduce bias in the parameter estimates for better statistical inference to help informed decision-making (Dubé & Legros, 2014;Pace, Barry, Gilley, & Sirmans, 2000;Smith & Wu, 2009;Sun, Tu, & Yu, 2005). Additional to its central aim of extending the property price equation to include design quality determinants, this paper builds upon previous hedonic studies based on design quality through an applied methodology that accounts for the spatial and temporal dimensions of property transaction data.…”

Modeling Spatial Data Pooled over Time: Schematic Representation and Monte Carlo Evidences

“…As pointed out by some authors [8]- [11] [25], the construction of a temporal weights matrix can help to build spatio-temporal weights matrices. However, this decomposition can also be simplified by introducing constraints on the spatial weights matrix through a block diagonal decomposition.…”

“…This constraint implies that working with spatial econometric tests and models with spatial data pooled over time assumes that the assumption of perfection anticipation is true (or likely). This lack of constraint on the weights matrix can potentially lead to a bias in the estimation, as noted by Smith (2009) [11], related to over-connection of the matrix.…”

“…Recent developments have proposed a simple way to adequately control the temporal dimension while accounting for spatial dimension influencing the data generating process [8]- [11] when data consist of spatial observations pooled over time. However, very few have investigated the effect of adopting spatial econometric techniques for such data.…”