Bayesian analysis of Hong Kong's housing price dynamics

Abstract

This paper uses a Bayesian vector‐autoregressive model with sign restrictions to estimate the underlying drivers of Hong Kong's housing price dynamics in the short run. While existing studies are useful in analysing housing valuation, little attention has been paid to the short‐run dynamics. In contrast, the present paper identifies short‐run drivers of housing prices using structural identification with theoretical underpinnings. We find that among the shocks that we have identified, bank lending shock and housing supply shock were the main factors affecting Hong Kong's housing prices. Low mortgage rates were another key factor that led to the significant increase in housing prices after the global financial crisis.

How does Covid-19 affect global equity markets?

“…The income-SARS gradient can be accounted for by controlling for pre-SARS housing values but not an array of measurable living conditions. Wu et al ( 2017 ) apply a Bayesian Vector-Autoregressive (BVAR) model with sign restrictions to estimate the underlying drivers of Hong Kong’s housing price dynamics during the period 1996–2016. They find that shocks to bank lending, housing supply, and housing demand are the main contributors to the fall in housing price during the Asian financial crisis–SARS episode.…”

A robust textual analysis of the dynamics of Hong Kong property market

“…Among the various factors driving property price dynamics, the supply‐side factor is widely considered a key contributing factor to the uptrend in Hong Kong’s housing prices (Wu et al, 2017). With the Hong Kong Government suspending regular land sales in 2003, and with a low share of residential land use in total land area (7% in 2018, according to the Hong Kong Planning Department), new completion in Hong Kong has stayed at relatively low levels, despite some pick‐ups in recent years following the resumption of regular land sales in 2010.…”

“…This paper is related to Wu et al (2017), who use a Bayesian vector-autoregression model to estimate the short-run housing market dynamics in Hong Kong. Their analysis suggests that sentiments could account for approximately 8% of housing price variations.…”

Special issue on housing and financial stability: An introduction

“…According to many measures, the housing market in Hong Kong has become increasingly unaffordable, suggesting the possibility of a bubble (Forbes, ; CNBC, ; see also Leung & Tang, , ). Taking advantage of Hong Kong time‐series data, Wu, Cheng, and Wong () estimate a Bayesian vector autoregression and employ the sign restriction methodology to analyse the empirical determinants of housing prices in Hong Kong. Interestingly, on top of the traditional variables such as interest rates and bank loans, they find that almost half of the time‐series fluctuations in Hong Kong housing prices cannot be explained.…”