Housing price indices for small spatial units

Housing price indices from online listing data: Addressing the spatial bias with sampling weights

“…As a result, matched treated and control samples of housing posting data are constructed by using a Mahalanobis Matching with propensity scores as calipers, first introduced to the literature by Rosenbaum and Rubin (1985). For a more contemporaneous application and further details on the method, please see (Lopez and Hewings, 2018: pp. 59–61).…”

“…Finally, the third challenge entails considering the changing nature of housing preferences and how it is captured in housing posting data. To solve this issue and following previous literature (Lopez and Hewings, 2018; Lopez and Aroca, 2012; Paredes 2011), this paper proposes using a Fisher housing price index (Fisher, 1921) calculated using the matched samples at each point in time based on hedonic regressions (Rosen, 1974) such aswhere lny is the natural logarithm of the housing price, normalΧk is a matrix of housing characteristics. In equations (4) and (5), the subscripts t0 and t1 are used to denote the sample of matched housing posts in the control and treated periods respectively.…”

Is There a Slope Discount?

“…More details about computing property price indices can be found in Bailey et al (1963) [13], Bourassa et al (2006) [14], Clapp and Giaccotto (1992) [15], De Vries et al (2009) [16], Wallace and Meese (1997) [17], Wood et al (2005) [18], and Shiller (1991) [19]. Most recently, Lopez and Hewings (2018) [20] also introduced a method that is based on the repeat-sales (i.e., Case-Shiller) method, while being more flexible; this idea was first suggested by McMillen (2012) [21]. The hedonic regression method is typically the best approach for constructing a constant-quality residential property price index.…”

Micro-geographic property price and rent indices

“…Instead, we refer to European Commission (2013) for an overview. Recent notable developments in this literature are the use of matching approaches (Lopez and Hewings, 2018) to broaden samples beyond repeat sales (Bailey et al, 1963), adaptive weights smoothing to produce land value surfaces (Kolbe et al, 2015), or machine learning to capture otherwise unobservable housing characteristics (Shen and Ross, 2021). This strand of research is a manifestation of a broader trend to fit flexible functional forms to data in a way that supports out-of-sample predictions.…”