House Prices from Magazines, Realtors, and the Land Registry

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Date Published 2012
Primary Author Chihiro Shimizu
Other Authors Kiyohiko G. Nishimura, Tsutomu Watanabe
Theme House Price Indices


In constructing a housing price index, one has to make several non-trivial choices. One of them is the choice among alternative estimation methods, such as repeat-sales regression, hedonic regression, etc. There are numerous papers on this issue, both theoretical and empirical. Shimizu et al. (2010), for example, conduct a statistical comparison of several alternative estimation methods using Japanese data. However, there is another important issue which has not been discussed much in the literature, but which has been regarded as critically important from a practical viewpoint: the choice among different data sources for housing prices. There are several types of datasets for housing prices: datasets collected by real estate agencies and associations; datasets provided by mortgage lenders; datasets provided by government departments or institutions; and datasets gathered and provided by newspapers, magazines, and websites. Needless to say, different datasets contain different types of prices, including sellers' asking prices, transaction prices, valuation prices, etc. With multiple datasets available, one may ask several questions. Are these prices different? If so, how do they differ from one another? Given the specific purpose of the housing price index one seeks to construct, which dataset is the most suitable? Alternatively, with only one dataset available in a particular country, one may ask whether this is suitable for the purpose of the index one seeks to construct. This paper is a first attempt to address some of these questions. Specifically, in order to do so, we will conduct a statistical comparison of different house prices collected at different stages of the house buying/selling process. To conduct this exercise, we focus on four different types of prices: (1) asking prices at which properties are initially listed in a magazine, (2) asking prices when an offer for a property is eventually made and the listing is removed from the magazine, (3) contract prices reported by realtors after mortgage approval, and (4) registry prices. We prepare datasets of these four prices for condominiums traded in the Greater Tokyo Area from September 2005 to December 2009. The four prices are collected by different institutions and therefore recorded in different datasets: (1) and (2) are collected by a real estate advertisement magazine; (3) is collected by an association of real estate agents; and (4) is collected jointly by the Land Registry and the Ministry of Land, Infrastructure, Transport and Tourism. An important advantage of prices at earlier stages of the house buying/selling process, such as initial asking prices in a magazine, is that they are likely to be available earlier, so that house price indices based on these prices become available in a timely manner. The issue of timeliness is important given that it takes more than 30 weeks before registry prices become available. On the other hand, it is often said that prices at different stages of the buying/selling process behave quite differently. For example, it is said that when the housing market is, say, in a downturn, prices at earlier stages of the buying/selling process, such as initial asking prices, will tend to be higher than prices at later stages. Also, it is said that, for various reasons, prices at earlier stages contain non-negligible amounts of “noise.” For instance, prices can be renegotiated extensively before a deal is finalised, and not all of the prices appearing at earlier stages end in transactions, for example, because a potential buyer's mortgage application is not approved. The main question of this paper is whether the four prices differ from each other and, if so, by how much. We will focus on the entire cross-sectional distribution for each of the four prices to make a judgment on whether the four prices are different or not. The cross-sectional distributions for the four prices may differ from each other simply because the datasets in which they are recorded contain houses with different characteristics. For example, the dataset from the magazine may contain more houses with a small floor space than the registry dataset, which may give rise to different price distributions. Therefore, the key to our exercise is how to eliminate quality differences before comparing price distributions. We will conduct quality adjustments in two different ways. The first is to use only the intersection of two different datasets, that is, observations that appear in two datasets. For example, when testing whether initial asking prices in the magazine have a distribution similar to that of registry prices, we first identify houses that appear in both the magazine dataset and the registry dataset and then compare the price distributions for those houses in both datasets. The second method is based on hedonic regressions.

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