While the outbreak of the COVID-19 disease has caused asset markets to experience an unprecedented spike of risk and uncertainty worldwide, the real estate market in many global cities appears to be immune to the adverse effects. How does COVID-19 affect urban housing markets? This study is a first attempt to identify the pandemic’s impact on house prices by applying a price gradient analysis to the COVID-19 epicentre in China. Considering microlevel housing transaction data in 62 areas from nine districts in Wuhan City from January 2019 to July 2020, the hedonic pricing and the price gradient models suggest that there was, respectively, a 4.8% and a 5.0–7.0% year-on-year fall in house prices immediately after the pandemic outbreak. Although house prices rebounded after the lockdown period, the gradient models show that the price gradients were flattened from the epicentre to the urban peripherals. The price premiums in high-density areas were also substantially discounted after the city’s lockdown. Our findings are robust to different model specifications. The implication is that the risk associated with the pandemic is localised and transitory in nature. People may be able to internalise the risk by residing in low-density residential areas.

Property technology has ushered in new possibilities for the real estate industry, including the use of virtual reality (VR) technologies by real estate agents when marketing properties. Previous studies have identified factors in determining purchase intents within a virtual setting, but little is known about how such technologies affect homebuyers’ purchase decisions. In this study, a family-home purchase decision-making model is used to conceptualise how VR can affect buyers’ involvement in the property purchasing process. Using transaction data from Wuhan City, China, the study found that a 1% increase in the number of followers visiting an online property portal resulted in a 21% increase in physical home visits. The study also indicates that VR tours shorten the marketing time of property by 6.4% and narrow the bid-ask spread by 2%, ceteris paribus. These findings suggest that emerging property technologies such as VR can enhance the purchase decision-making process and reshape the role of the real estate agent.

Buyers in the property market often use an agent who is employed by the seller to assist their home searches. This unique and widely used agency arrangement in the property market is known as “sellers’ agents”. While principal-agent theory advocates that buyers should directly hire their agents (i.e., buyers’ agents) to do the home-hunting, search theory however implies that sellers would employ their agents (i.e., sellers’ agents) to increase the probability of the sale and selling prices. Although sellers’ agents and buyers’ agents are two very distinct institutions, many previous studies assume that their agency characteristics are identical and provide limited insights on how such a seemingly subtle but crucial agency arrangement affects transaction outcomes. Using transaction data from Wuhan China, this study disentangles the effects of the buyers’ and sellers’ agents on properties’ selling prices and their time on the market. The findings indicate that on average transactions conducted by sellers’ agents will be associated with a significant selling price premium of around 3.4%. As a critical test, we further show that the transactions completed by sellers’ agents with selling price premiums will have a shorter marketing time than those completed by buyers’ agents.

Commuting behaviour has been intensively examined by geographers, urban planners, and transportation researchers, but little is known about how commuting behaviour is spatially linked with the job and housing markets in urban cities. New Zealand has been recognised as one of the countries having the most unaffordable housing over the past decade. A group of middle-class professionals called ‘key workers’, also known during the pandemic as ‘essential workers’, provide essential services for the community, but cannot afford to live near their workplaces due to a lack of affordable housing. As a result, these key workers incur significant sub-optimal commuting. Such job-housing imbalance has contributed to a so-called spatial mismatch problem. This study aims to visualise the excess commuting patterns of individual workers using the Integrated Data Infrastructure (IDI) from Statistics New Zealand. The visualisation suggests that over the last demi-decade, housing unaffordability has partially distorted the commuting patterns of key workers in Auckland. More of the working population, in particular those key workers, are displaced to the outer rings of the city. While there is an overall reduction in excess commuting across three groups of workers, key workers remain the working population with a disproportionate long excess commute.

Understanding risk-adjusted returns in real estate investment are crucial, but little is known about the risk-adjusted returns for direct real estate. This paper examines risk-adjusted total returns by developing an extended capital asset pricing model (CAPM) to investigate whether direct real estate returns compensate for their risk levels. Based on a panel dataset of the residential property transaction in 62 Territorial Authorities of New Zealand from 2002Q1 to 2018Q4, a direct real estate portfolio performance in the single-factor CAPM model is compared with the national housing markets stock markets and REITs markets in New Zealand before the pandemic. The results demonstrate that the direct real estate returns outperform the market returns with a significant positive alpha and beta smaller than one but positive. The alpha is further evaluated by the five-factor CAPM model, which includes the factors of liquidity risk, value risk, time risk, credit-rating risk, and currency risk. The assessment shows that most of the excess return (alpha) can be attributed to direct real estate market risks.