Rent index forecasting through neural networks

PurposeChinese housing market has been growing fast during the past decade, and price-related forecasting has turned to be an important issue to various market participants, including the people, investors and policy makers. Here, the authors approach this issue by researching neural networks for rent index forecasting from 10 major cities for March 2012 to May 2020. The authors aim at building simple and accurate neural networks to contribute to pure technical forecasting of the Chinese rental housing market.Design/methodology/approachTo facilitate the analysis, the authors examine different model settings over the algorithm, delay, hidden neuron and data spitting ratio.FindingsThe authors reach a rather simple neural network with six delays and two hidden neurons, which leads to stable performance of 1.4% average relative root mean square error across the ten cities for the training, validation and testing phases.Originality/valueThe results might be used on a standalone basis or combined with fundamental forecasting to form perspectives of rent price trends and conduct policy analysis.

House price and household consumption in China: evidence from micro-level data

Purpose This paper aims to identify how house price affects household consumption. Design/methodology/approach The authors use a micro-level data set that tracks the house price and consumption of a vast number of households over a period of four years. OLS regression is the main econometric method. Findings The authors document robust evidence that an increase in house prices stimulates household consumption, regardless of whether a household owns or rents. Moreover, the authors find that both acquiring and losing homeownership negatively affects household consumption. Further investigation suggests significant regional heterogeneity in the relationship between house prices and household consumption. Originality/value This is one of the first studies examining the relationship between house price and household consumption in China using micro-level data. Given the uniqueness of the Chinese housing market and China’s fast-growing consumption rate, the study contributes new evidence to the long-lasting debate.

The Chinese Housing Market: Are Homes Mispriced and Can we Expect a Crash

As prices and vacancy rates skyrocket, the Chinese housing market inspires speculation that a market correction would ripple to a global economic slowdown. This paper draws on available market data and studies the unique aspects of the Chinese housing market to determine whether Chinese home prices are overpriced, and if such a mispricing poses any threat to the global economy.

Identifying Bubbles in China’s Property Market for Consumer Financial Well-Being

A sharp increase in Chinese house prices combined with the extraordinary lending growth during the 2000 s has led to concerns of an emerging real estate bubble and impairment of consumer financial well-being. This article studies real house prices relative to fundamental house values. Housing constitutes a large fraction of most household portfolios therefore affect household well-being, and its characteristics are in contrast to what prevails in most financial markets as arbitrage is limited, and hence correction toward fundamental values can be a prolonged process. Using a time-varying present value approach, our findings suggest evidence of bubbles in the Chinese housing market nationally and in representative cities using real-term data. We also find that price dynamics have an important role to play in determining house prices. Moreover, the results reveal that the dominant driving force of house price deviations from fundamental values might be the less than fully rational behavior of investors rather than fundamental factors. This seems plausible in an emerging market such as China.