Big data, accessibility and urban house prices. (November 2021)

• Record Type: Journal Article
• Authors: Bourassa, Steven C; Hoesli, Martin; Merlin, Louis; Renne, John
• Other Names: Bannister Jon guest-editor.; O'Sullivan Anthony guest-editor.
• Is Part Of: Urban studies. Volume 58:Number 15(2021)
• Page Start: 3176
• Page End: 3195
• Publication Date: 2021-11
• Subjects: accessibility indices -- big data -- hedonic models -- property valuation -- spatial models; 可达性指数 -- 大数据 -- 享乐模型 -- 房地产估值 -- 空间模型; Cities and towns -- Periodicals; City planning -- Periodicals; 307.1216
• DOI: 10.1177/0042098020982508
• Languages: English
• ISSNs: 0042-0980

Abstract

Big data applications are attracting increasing interest among urban researchers. One unexplored question is whether the inclusion of big data accessibility indices improves the accuracy of hedonic price models used for residential property valuation. This paper compares a big data index with an index derived from a regional travel demand model developed by local transportation planning agencies and traditional measures of accessibility defined as distances to employment centres. Controls for submarkets and a combined spatial autoregressive and spatial error model are also assessed as tools for capturing the value of location. Using single-family residential transactions from the Miami, Florida, metropolitan area, the study's main conclusion is that the big data accessibility measure does not add meaningful explanatory or predictive power. In contrast, the spatial autoregressive and error model outperforms the other options considered.