Morris A. Davis

The price and quantity of residential land in the United States

A house is a bundle comprising a physical structure and the plot of land upon which the house is built. Thus changes in house prices reflect changes in the cost of structures and value of land. In this paper we apply this insight to construct the first constant-quality price and quantity indexes for the aggregate stock of residential land in the United States. We document that the value of residential land exceeds annual GDP, and that the dynamics for the prices of residential land and residential structures are quite different. For example, the real price index for residential land almost tripled between 1975 and 2005, while the real price of structures increased by only 24 percent. Fluctuations in house prices at business cycle frequencies, including the recent boom, are primarily driven by changes in the price of land.

The price of residential land in large US cities

Combining data from several sources, we build a database of home values, the cost of housing structures, and residential land values for 46 large US metropolitan areas from 1984 to 2004. Our analysis of these new data reveal that since the mid-1980s residential land values have appreciated over a much wider range of cities than is commonly believed. And, since 1998, almost all large US cities have seen significant increases in real residential land prices. Averaging across the cities in our sample, by year-end 2004, the value of residential land accounted for about 50 percent of the total market value of housing, up from 32 percent in 1984. An implication of our results is that housing is much more land intensive than it used to be, meaning that the future course of home prices--the average rate of appreciation and volatility--is likely to be determined even more by demand factors than was the case even ten or twenty years ago.

A primer on the economics and time series econometrics of wealth effects

This paper reviews the statistical approach typically applied by macroeconomists to investigate the empirical links among aggregate data on household consumption, income, and wealth. In particular, we focus on studies determining whether and how much changes in net worth, such as those generated by the stock-market boom in the U.S. over the latter 1990s, are responsible for subsequent swings in the growth rate of consumer spending. We show how simple economic theory is used to motivate an econometric strategy that consists of two stages of analysis. First, regressions are used to identify trend movements shared by consumption, income, and wealth over the long run, then deviations of these series from their commong long- run trends are used to help forecast consumption growth over the short run. Our discussion highlights the various judgments that researchers must make in the course of implementing this empirical approach, and we detail how specific parameter estimates describing the magnitude of the wealth effect on consumption--and even broad conclusions about its existence--are affected by making alternative choices.

Macroeconomic Implications of Agglomeration

Cities exist because of the productivity gains arising from clustering production and workers, a process called agglomeration. How important is agglomeration for aggregate growth? This paper constructs a dynamic stochastic general equilibrium model of cities and uses it to estimate the effect of local agglomeration on aggregate growth. We combine aggregate time series and city-level panel data to estimate our model’s parameters by the Generalized Method of Moments. The estimates imply that local agglomeration has an economically and statistically significant impact on the growth rate of per capita consumption, raising it by about 10 percent.

The Role of Housing in Labor Reallocation

This paper builds a dynamic general equilibrium model of cities and uses it to analyze the role of local housing markets and moving costs in determining the character and extent of labor reallocation in the US economy. Labor reallocation in the model is driven by idiosyncratic city-specific productivity shocks, which we measure using a dataset that we compile using more than 350 U.S. cities for the years 1984 to 2008. Based on this measurement, we find that our model is broadly consistent with the city-level evidence on net and gross population flows, employment, wages and residential investment. We also find that the location-specific nature of housing is more important than moving costs in determining labor reallocation. Absent this quasi-fixity of housing, and under various assumptions governing population flows, population and employment would be much more volatile than observed.

A Trend and Variance Decomposition of the Rent-Price Ratio in Housing Markets

We use the dynamic Gordon-growth model to decompose the rent-price ratio for owner-occupied housing in the U.S., four Census regions, and twenty-three metropolitan areas into three components: The expected present value of real rental growth, real interest rates, and future housing premia. We use these components to decompose the trend and variance in rent-price ratios for 1975-2005, for an early sub-sample (1975-1996), and for the recent housing boom (1997-2005). We have three main findings. First, variation in expected future real rents accounts for a small share of variation in our sample rent-price ratios; variation in real interest rates and housing premia account for most of the variability. Second, expected future real rates and housing premia were so strongly negatively correlated prior to 1997 that changes to real interest rates did not affect the rent-price ratio. After 1997, rates and premia have been positively correlated, and the decline in the rent-price ratio that has occurred in almost every geographic area in our sample since 1997 reflects both declining real rates and declining premia. Third, we show that in the recent housing boom, 65 percent of the decline in the aggregate rent-price ratio is due to a declining housing premium.

Housing, House Prices, and the Equity Premium Puzzle

Many recent papers have claimed that when housing services are treated separately from other forms of consumption in utility, a wide range of economic puzzles such as the equity premium puzzle can be explained. Our paper challenges these claims. The key assumption embedded in this literature is that households are not very willing to substitute housing services for consumption. We show that housing services and consumption must be much more substitutable than has been assumed for a neoclassical consumption model to be consistent with U.S. house price data. Further, when forced to match both historical house prices and stock returns, the lowest risk-free rate the model can generate is 11 percent.

Residential land values in the Washington, DC metro area: New insights from big data

We use new property-level data to estimate the price of land from 2000 to 2013 for nearly the universe of detached single-family homes in the Washington, DC metro area and characterize the housing boom-bust cycle in land and house prices at a fine geography. The data show that land prices were more volatile than house prices everywhere, but especially so in the areas where land was inexpensive in 2000. We demonstrate that the change in the land share of house value during the boom was a significant predictor of the decline in house prices during the bust, highlighting the value of focusing on land in assessing house-price risk.

Gross Migration, Housing and Urban Population Dynamics

Cities experience significant, near random walk productivity shocks, yet population is slow to adjust. In practice local population changes are dominated by variation in net migration, and we argue that understanding gross migration is essential to quantify how net migration may slow population adjustments. Housing is also a natural candidate for slowing population adjustments because it is dicult to move, costly to build quickly, and a large durable stock makes a city attractive to potential migrants. We quantify the influence of migration and housing on urban population dynamics using a dynamic general equilibrium model of cities which incorporates a new theory of gross migration motivated by patterns we uncover in a panel of US cities. After assigning values to the models parameters with an exactly identified procedure, we demonstrate that its implied dynamic responses to productivity shocks of population, gross migration, employment, wages, home construction and house prices strongly resemble those we estimate with our panel data. The empirically validated model implies that costs of attracting workers to cities drive slow population adjustments. Housing plays a very limited role.

Default when Current House Prices are Uncertain

We specify a new model of homeowner mortgage default. In our model, homeowners do not know the current price of their home until they sell; rather, they maintain an unbiased guess of the price and optimally update this guess as new information, such as the sale price of similar homes, is observed. Compared to the predictions of a model where homeowners know the current price with certainty, uncertainty about the price considerably reduces the probability homeowners default even when the current price is likely substantially less than the mortgage balance. We estimate model parameters using data on self-assessed house prices, house-price indexes and mortgage defaults. We find uncertainty about the current level of house prices reduced defaults for a cohort of prime mortgages issued in 2006 by 25 percent in 2010 and 2011.