Effects of Transportation Infrastructure and Location on Residential Real Estate Values: Application of Spatial Autoregressive Techniques

Abstract

Proximity to transportation infrastructure (highways and public transit) influences residential real estate values. Housing values also are influenced by propinquity to a shopping facility or a recreational amenity. Spatial autoregressive (SAR) models were used to estimate the impact of locational elements on the price of residential properties sold during 1995 in the Greater Toronto Area. A large data set consisting of 27,400 freehold sales was used in the study. Moran’s I was estimated to determine the effects of spatial autocorrelation that existed in housing values. SAR models, using a combination of locational influences, neighborhood characteristics, and structural attributes, explained 83 percent variance in housing values. Using the “comparable sales approach,” a spatiotemporal lag variable was estimated for every property in the database. This research discovered that SAR models offered a better fit than nonspatial models. This study also discovered that in the presence of other explanatory variables, locational and transportation factors were not strong determinants of housing values. On the other hand, the number of washrooms and the average household income in a neighborhood were found to be significant determinants of housing values. Stepwise regression techniques were used to determine reduced spatial hedonic models.

Price and Income Elasticities of Demand for Housing Characteristics in the City of Barcelona

“…This segmentation by districts is the first one to be used, each of the equations being The economic literature has focused, by means of capitalization, on the willingness to pay of some neighbourhood amenities such as parks, schools or undergrounds. In this framework, we can emphasize the contributions of Cheshire and Sheppard (1995), Bell and Man (1996), Bilbao-Terol (2000), Bogart and Cromwell (2000), Haider and Miller (2000), Gibbons and Machin (2001), Downes and Zabel (2002) Tse (2002), Anderson and West (2006), Cheshire and Sheppard (2004). However, the main aim of this paper is not to explain variability in housing prices due to the location but to estimate the price and income elasticities of demand for a series of basic housing characteristics (quantity, quality and location), so we decide to replace the amenities variables for the locational dummies.…”

Impacts of Beijing Bus Rapid Transit on Pre-owned Home Values

“…Many studies have investigated the impacts of transit-oriented development (TOD) on surrounding property values and reported positive results (Garrett 2004;McMillen and McDonald 2004;Weinstein et al 2014;Cervero and Duncan 2004;Haider and Miller 1995;Knaap, Ding, and Hopkins 2001). Of these studies, Chicago's Midway Line showed that the opening of new rail services increased housing prices, with rates of land-value appreciation varying over time (McMillen and McDonald 2004).…”

Costs of Right-of-Way Acquisition: Methods and Models for Estimation

“…Statistical regression models called hedonic price models are popular tools to estimate value (e.g., ten Siethoff and Kockelman, 2002, Vadali and Sohn, 2001, Carey, 2001, Haider and Miller, 2000, and Kockelman, 1997. These typically rely on structural characteristics, parcel size, and locational information.…”

“…His study of condominiums/townhouses explained less variation in prices (R 2 =0.646 vs. R 2 =0.795 for single-family homes) but suggested that buyers of condominiums and townhomes place a higher premium on access to major streets and freeways, than those buying single-family homes. Haider and Miller (2000) studied the effects of transportation infrastructure and location on real estate values for the Greater Toronto Area, using housing sales data from the Toronto Real Estate Board and census data. The data were spatially coded to create location variables for proximity to highways, subways, waterfront, and malls.…”