House price is affected by households’ expectation of future house price trend and volatility, where the expected volatility of housing capital return, indicated by variance, is defined as the housing market risk. Theoretically, risk element cannot be directly inserted in the standard housing models because most of the models are built on the underlying assumption of certainty. Extending the life-cycle model to a two-asset expected utility case with uncertainty, we show house price is affected by housing market risk premium, which is a function of households’ risk-aversion coefficient, real housing wealth, and expected housing volatility. Empirical analysis relying on China’s 2001–2018 provincial housing panel data supports the theoretical innovation. Despite the empirical results show that the effect of housing market risk on house price is tiny, simulations suggest that the consideration of risk is quite helpful in analyzing and predicting the long-run house price equilibriums.

This paper explores the properties of dynamic aggregate housing models. In conventional models, in response to demand shocks the primary adjustment mechanism is through prices and changes in housing supply. However, the size of the supply response depends on the price elasticity of supply and in countries such as the UK where the elasticity is low, house prices can rise sharply, worsening affordability. But this ignores the roles of housing risk and credit markets which affect the user cost of capital and the paper demonstrates that models that explicitly introduce a housing risk premium have an additional price stabiliser. The importance is shown through stochastic simulations; these simulations also demonstrate that conventional models used for forecasting and policy analysis may overstate future house price growth.

This study aimed to theoretically identify the impact factors of the financial market on house prices. Developed upon the two-asset model and with the consideration of risky financial assets, our three-asset model reveals a new derivation of house prices. Compared with the two-asset model, the newly emerged term is similar to the Sharpe β; therefore, it is a risk premium term. Based on China’s 2001–2018 panel data, theoretical derivations are examined. However, the short-term effect of this risk term on house prices is practically small. Given the nonlinear pattern, the long-term effect of the risk term is checked by repeated stochastic simulation. The results imply the following: (i) real house prices are nonlinearly affected by three financial market factors, namely, the expected financial market return, financial market volatility, and the correlation between housing and financial markets; (ii) the correlation determines the signs and the significance of the effects of the other two factors; and (iii) the naturally changed correlation causes periodic house price fluctuations. Therefore, to stabilize real house prices, it is recommended that the government control the money flow between the two markets.

There have been many recent fears of severe house-price decreases in some provinces in China causing a nationwide collapse of the housing market. Therefore, this paper aims to clarify the linkage structure of China's housing market and its risk contagion routes. Given monthly data of provincial housing and stock-market capital returns from 2001M01 to 2019M12, on the basis of graph theory, this paper first explores the linkage structure of provincial housing markets. Relying on the linkage structure, this paper then simulates the effect of unexpected negative shocks from the stock market on the probabilities of a housing-market collapse based on the epidemic model. The results show that (i) consistently with practical evidence, the probability of housing-market collapse is relatively high in the southwest of China and (ii) reducing housing-market linkage, such as through a blocking mechanism, to prevent collapse is helpful. IntroductionIn recent decades, house prices in most developing and developed countries have been sharply increasing. For example, the average house prices in China were 2409.61 and 9208.56 CNY/m 2 in 2001Q1 and 2019Q4, respectively (CNY 1 ≈ USD 0.15). This is a 282.16% nominal inflation during the last 19 years or 14.85% annual growth on average. However, after 2018, the growth rate of house prices in Communicated by Vladik Kreinovich.