On the Evolution of the House Price Distribution

2013

We investigate the cross-sectional distribution of house prices in the Greater Tokyo Area for the period 1986 to 2009. We find that size-adjusted house prices follow a lognormal distribution except for the period of the housing bubble and its collapse in Tokyo, for which the price distribution has a substantially heavier right tail than that of a lognormal distribution. We also find that, during the bubble era, sharp price movements were concentrated in particular areas, and this spatial heterogeneity is the source of the fat upper tail. These findings suggest that, during a bubble period, prices go up prominently for particular properties, but not so much for other properties, and as a result, price inequality across properties increases. In other words, the defining property of real estate bubbles is not the rapid price hike itself but an increase in price dispersion. We argue that the shape of cross sectional house price distributions may contain information useful for the detection of housing bubbles.

International Journal of Modern Physics: Conference Series, 2012

How can we detect real estate bubbles? In this paper, we propose making use of information on the cross-sectional dispersion of real estate prices. During bubble periods, prices tend to go up considerably for some properties, but less so for others, so that price inequality across properties increases. In other words, a key characteristic of real estate bubbles is not the rapid price hike itself but a rise in price dispersion. Given this, the purpose of this paper is to examine whether developments in the dispersion in real estate prices can be used to detect bubbles in property markets as they arise, using data from Japan and the U.S. First, we show that the land price distribution in Tokyo had a power-law tail during the bubble period in the late 1980s, while it was very close to a lognormal before and after the bubble period. Second, in the U.S. data we find that the tail of the house price distribution tends to be heavier in those states which experienced a housing bubble. We also provide evidence suggesting that the power-law tail observed during bubble periods arises due to the lack of price arbitrage across regions.

2016

We all know that housing prices have followed a boom-and-bust trajectory over the past fifteen years, but which segments of the population experienced the sharpest rise and fall—and in which parts of the country? Using transaction-level data from multiple large urban counties, I analyze the entire distribution, breaking down the change in housing prices into quantiles. I measure the change in the distribution in house prices in each city, determine how much of the change can be explained by quality variables, and investigate what differences between the cities might be causing the variation in their housing price distributions—especially during the housing bubble, which some cities experienced more acutely than others. This analysis allows me to identify which segments of the population were most sensitive to the boom-andbust—and in which cities—with policy implications for the role of the housing market in social equity and financial stability going forward. **The author can be con...

Journal of Economics and Statistics, 2010

Do indexes of house prices behave differently depending on the estimation method? If so, to what extent? To address these questions, we use a unique dataset that we compiled from individual listings in a widely circulated real estate advertisement magazine. The dataset contains more than 470,000 listings of housing prices between 1986 and 2008, including the period of the housing bubble and its burst. We find that there exists a substantial discrepancy in terms of turning points between hedonic and repeat sales indexes, even though the hedonic index is adjusted for structural changes and the repeat sales index is adjusted in the way Case and Shiller suggested. Specifically, the repeat sales measure signals turning points later than the hedonic measure: for example, the hedonic measure of condominium prices bottomed out at the beginning of 2002, while the corresponding repeat sales measure exhibits a reversal only in the spring of 2004. This discrepancy cannot be fully removed even if we adjust the repeat sales index for depreciation. JEL Classification Number : C43; C81; R21; R31

The Quarterly Review of Economics and Finance, 2018

We assess the goodness-of-fit of multiple possible distributions to real estate price data from Charleston County, South Carolina.  We find that the best fit distribution lies somewhere between the lognormal and power law distributions.  We find that some evidence that distributional information is correlated with the presence of a price "bubble" in the real estate market.

We explore the house price distributions for the English cities of London, Manchester, Bristol, Newcastle, Birmingham and Leeds. We find Pareto (power law) behaviour in their upper tails, which is clearly distinct from lognormal and gamma distributions in the cases of London, Manchester and Newcastle. For London, the city with the lowest power, this is a striking match with that found in the wealth distribution of the super-rich. We propose an index of Housing Wealth Inequality based on the Pareto exponent and analogous to the Gini coefficient, and comment on its possible uses.

SSRN Electronic Journal, 2013

To what extent do house price dynamics differ across market segments? And what determines this heterogeneity? We address these questions by analysing a data set of individual houses and mortgages, based on a survey of about 2,000 Dutch households over the period 2003-2011. We estimate a dynamic panel data model of house price dynamics by means of the Arellano-Bond estimator. Three main empirical results emerge. First, we generally find that house price dynamics imply a convergence towards their long-run equilibrium value, as indicated by a negative serial correlation coefficient and a positive estimated mean reversion coefficient. Second, there is evidence that the housing market in the Netherlands is inefficient. Third, there is important heterogeneity across different market segments. We document that the speed of convergence of house price dynamics and the efficiency of housing markets depends on the geographical location and degree of urbanization, the type and year of construction of a house, the type of mortgage financing and households' sentiment about the medium-term outlook for income.

The housing prices in many Asian cities have grown rapidly since mid-2000s, leading to many reports of bubbles. However, such reports remain controversial as there is no widely accepted definition for a housing bubble. Previous studies have focused on indices, or assumed that home prices are lognomally distributed. Recently, Ohnishi et al. showed that the tail-end of the distribution of (Japan/Tokyo) becomes fatter during years where bubbles are suspected, but stop short of using this feature as a rigorous definition of a housing bubble. In this study, we look at housing transactions for Singapore (1995 to 2014) and Taiwan (2012 to 2014), and found strong evidence that the equilibrium home price distribution is a decaying exponential crossing over to a power law, after accounting for different housing types. We found positive deviations from the equilibrium distributions in Singapore condo-miniums and Zhu Zhai Da Lou in the Greater Taipei Area. These positive deviations are dragon kings, which thus provide us with an unambiguous and quantitative definition of housing bubbles. Also, the spatial-temporal dynamics show that bubble in Singapore is driven by price pulses in two investment districts. This finding provides a valuable insight for policymakers on implementation and evaluation of cooling measures.

International Economic Review, 2013

Some salient stylized facts of the housing sector are hard to reconcile with the Walrasian market paradigm. These include the co-movement of prices and sales and their negative correlation with average time on the market. We build a search equilibrium model of the housing market which captures the illiquidity of housing assets. In the model, agents experience idiosyncratic shocks that affect how much they value their residence (e.g. their job or their size changes over time). When hit by a shock, agents become mismatched and seek to move, but they take time to locate an appropriate unit. Competitive forces operate in the housing market since by posting lower prices sellers get more visits and sell their property faster. We characterize a stationary competitive search equilibrium when the housing stock is fixed. We calibrate our economy to reproduce selected aggregate statistics of the U.S. economy. We find that the model's predictions are consistent with the observed joint behavior of prices, sales and time on the market. This is not the case when we consider the frictionless Walrasian version of the model.