Real estate listings and their usefulness for hedonic regressions

Abstract

Real estate platforms provide a new source of data which has already been used as a substitute for transaction data in hedonic regression applications. This paper asks whether it is valid to do so in the established research areas of (1) willingness to pay estimation, (2) automated valuations, and (3) price index construction. It therefore compares listings and transaction data and regression results derived from them. We find that ask prices stochastically dominate sale prices, mainly because the composition of characteristics differs between the two data sets. But estimates of implicit prices also differ. As a result, willingness to pay estimates from listings data can be widely off when compared with estimates from transaction data. Listings data are not very useful to predict market values of individual houses either, as these predictions suffer from upward bias and large error variance. We find, however, that an ask price index complements a sale price index, as it is useful for nowcasting.

Early Real Estate Indicators during the COVID-19 Crisis

“…Our cleaning process (especially selecting only the last price before a property leaves the platform) shifts the distribution of the list-price data towards that of transaction prices; this concerns in particular the highly-priced right tail of the listing data sets. With this process we find a closer correspondence between list and transaction data than others in the literature (see e.g., Shimizu 2016; Kolbe et al 2021). Interestingly, on the left-hand side of the price distributions transaction prices lie above listing prices.…”

“…This reluctance by sellers to reduce prices during a market down-turn also implies that list-prices are not always good indicators of market turning points (Knight et al 1994). This asymmetry in list-price reaction to underlying market tendency probably explains why the literature provides a mixed verdict on whether list prices are good leading indicators for transaction prices: While some researchers find them to be good leading indicators (see e.g., Ahmed et al 2016;Anenberg and Laufer 2017;Lyons 2019) others find the opposite (Kolbe et al 2021).…”

Influence of the Cadastral Value of the Urban Land and Neighborhood Characteristics on the Mean House Mortgage Appraisal

“…The choice of the models herein employed is justified by the principle of parsimony: if the model shows good performance, the simpler, the better. In fact, all these models co-exist in the recent literature [18][19][20][21][22][23].…”

“…In Istanbul, the spatial correlation problems that appeared in standard hedonic regression were reduced when controlling for factors of spatial dependence and district-level data, such as area, the total number of floors, construction process, share social services and high-rise residential complexes [19]. Comparisons have been made of hedonic models, estimated for home listing prices and purchase-sale house prices in Berlin [20] using characteristics of houses with the willingness-to-pay estimation. In Taiwan, hedonic models have been compared to spatial hedonic models [21] to analyze the urban blight effect on housing prices.…”

“…Nonetheless, home purchase-sale prices have been used in all models [2,5,12,14], and the few exceptions are home listing prices [20,24] when purchase-sale prices are not available, rent values [18,25] and cadastral values [26]. Listing data are readily available from platforms, but some data might not be very useful for predicting market values because they can be upwardly biased and wide variations may appear [20]. The cadastral value is defined by the Cadastre, formally known in English as the Spanish Directorate General for Cadastre (DGC) [27], for tax purposes.…”