 Housing price indices from online listing data: Addressing the spatial bias with sampling weightsEsteban Lopez Ochoa Environment and Planning B, 2023, vol. 50, issue 4, 1039-1056 Abstract: This paper proposes a method to reduce the inherent sampling bias when estimating housing price indices using online listing data. Producing more accurate and representative metrics is important as new sources of data emerge with higher frequency, detail, and volume, providing more information for policymaking, but usually come with strong sampling biases that are often overlooked. In the case of housing price indices, although the literature around its estimation is abundant, it has concentrated only in traditional and formal sources of housing data, which is normally not available in some markets (i.e. renting) and locations (developing countries). In this paper, I propose a method to create a housing price index (HPI) that is comparable in quality to the industry-standard Case-Shiller HPI but using online listing data. Using online listing data from a developing economy (Chile), this paper shows that large sampling biases present when using raw unweighted data, how these biases can be minimized using sampling weights, and how new and relevant information can be obtained from adjusted HPIs that can lead better policymaking. Keywords: Housing Price Indices; sampling bias; sampling weights; online listing data (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:50:y:2023:i:4:p:1039-1056 DOI: 10.1177/23998083221130713 Access Statistics for this article More articles in Environment and Planning B |
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