Power Laws in Real Estate Prices During Bubble Periods

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.

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.

2010

Is the cross-sectional distribution of house prices close to a (log)normal distribution, as is often assumed in empirical studies on house price indexes? How does it evolve over time? How does it look like during the period of housing bubbles? To address these questions, we investigate the cross-secional distribution of house prices in the Greater Tokyo Area. Using a unique dataset containing individual listings in a widely circulated real estate advertisement magazine in 1986 to 2009, we find the following. First, the house price, Pit, is characterized by a distribution with much fatter tails than a lognormal distribution, and the tail part is quite close to that of a power-law or a Pareto distribution. Second, the size of a house, Si, follows an exponential distribution. These two findings about the distributions of Pit and Si imply that the the price distribution conditional on the house size, i.e., Pr(Pit | Si), follows a lognormal distribution. We confirm this by showing that size adjusted prices indeed follow a lognormal distribution, except for periods of the housing bubble in Tokyo when the price distribution remains asymmetric and skewed to the right even after controlling for the size effect.

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.

SSRN Electronic Journal, 2014

This paper studies U.S. house prices across 45 metropolitan areas from 1980-2012. It applies the Gordon dividend discount model as a measure of long run "fundamentals", and uses mean group and pooled mean group estimation to get long run and short run estimates of determinants of house prices. We find great similarity across cities in that the long run house prices are largely explained by the same fundamentals, but adjust to the fundamentals slowly, at a rate of around 10% per year. We find sharp differences in short run adjustments (momentum) across cities, and the differences are correlated with local supply elasticities. Analysis of residuals suggests strong cyclical deviations. The bubble period (2000-2006) was longer than usual and was extended after 2002 when it looked to be dying out, in a way that is coincident with the rise in subprime securitization from 2003-2006.

2010

Japan and the United States have experienced the housing bubbles and subsequent collapses of the bubbles in succession. In this paper, these two bubbles are compared and the following findings are obtained.

IT-Incidents Management & IT-Forensics, 2000

Asset price bubbles: the implications for …, 2003

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...