Using a comprehensive database of corporate relocation events in the United States from 1994 to 2017, we investigate the impact of headquarters relocation on the local economy by examining its spillover effects on the housing market. We find that headquarters relocation into a district at zip code level leads to 10% higher housing price growth. We also document a temporal spillover effect whereby housing prices increase one year before the relocation and rise further until two years afterward and a significant spatial spillover effect of corporate relocation on nearby zip codes’ housing markets up to 15 miles. We show the positive spillover effect on housing price growth is more pronounced for relocating firms with larger employee sizes and economic bases. We further find local economic spillover and agglomeration economies associated with corporate relocation. Overall, our study indicates that corporate relocation exhibits a significant impact on the local housing market. This paper was accepted by Agostino Capponi, finance. Funding: The project received research grant from the Alrov Institute for Real Estate Research. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.01819 .

Understanding the intracity heterogeneities in housing market dynamics across microgeographic areas is important but challenging due to infrequent transactions. Unlike traditional methods that use trend-based clustering to improve the accuracy of local housing price and rent indices, we propose a novel hybrid model that combines the state-space model and the Bayesian nonparametric clustering approach to cluster neighbourhoods according to their temporal price volatility. We show that our methods improve the performance of traditional methods by 10-40%, using over 889,428 housing transactions in Singapore between 2006 and 2018. We also demonstrate a practical application of our method - monitoring neighbourhoods' distinct market reactions to macroeconomic or policy shocks, which has important implications for urban planning and housing investment.

Understanding the intracity heterogeneities in housing market dynamics across microgeographic areas is important but challenging due to infrequent transactions. Unlike traditional methods that use trend-based clustering to improve the accuracy of local housing price and rent indices, we propose a novel hybrid econometric and machine learning model that nonparametrically clusters neighbourhoods according to their temporal price volatility. We show that our methods improve the performance of traditional methods by 10-40%, using over 889,428 housing transactions in Singapore between 2006 and 2018. We also demonstrate a practical application of our method—monitoring neighbourhoods' distinct market reactions to macroeconomic or policy shocks, which has important implications for urban planning and housing investment.

Uncertainty about the inner workings of machine learning (ML) models holds back the application of ML-enabled systems in real estate markets. How do ML models arrive at their estimates? Given the lack of model transparency, how can practitioners guarantee that ML systems do not run afoul of the law? This article first advocates a dedicated software testing framework for applied ML systems, as commonly found in computer science. Second, it demonstrates how system testing can verify that applied ML models indeed perform as intended. Two system-testing procedures developed for ML image classifiers used in automated valuation models (AVMs) illustrate the approach.

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This article examines the impact of mainland Chinese buyers in the Hong Kong housing market, using complete transaction records between 2001 and 2017. We find that mainland buyers pay an average price premium of 1.4% compared with locals. The premiums are estimated to be 3.5% for large-sized luxury units and 1.6% for homes in central locations. The mechanisms that underlie the price premiums include a hedging effect, residential sorting, and information barriers, of which the hedging motive has the strongest impact. Mainland buyers’ price premiums rise significantly when the Chinese currency depreciates or China Economic Policy Uncertainty increases. Our study sheds light on the impact and mechanism of the ““China shock” on the global housing markets.

This paper investigates the impact of floods on inter-county migration, using the 2006-2019 Integrated Public Use Microdata Series of the American Community Survey. Exploiting variations in flood timing as a quasi-natural experiment, we use a difference-in-differences method to show that floods cause 2.7% and 1.9% increases in outflow and inflow migration, respectively. They trigger younger, better-educated, and employed residents out of, and attract older, less-educated, and unemployed ones into affected counties. Such patterns can be amplified by media sentiment on flood risks. The selective migration induces decreases in housing prices and increases in housing rent, respectively, suggesting a structural change in the housing markets of flood-prone regions. A back-of-envelope calculation shows net annual losses of $9.3 million and $1.98 million due to flood-induced selective migration, conditional on education and age profiles, respectively. Our results shed light on how information provision interacts with migration incentives in wake of natural disasters.

To meet widely recognised carbon neutrality targets, over the last decade metropolitan regions around the world have implemented policies to promote the generation and use of sustainable energy. Nevertheless, there is an availability gap in formulating and evaluating these policies in a timely manner, since sustainable energy capacity and generation are dynamically determined by various factors along dimensions based on local economic prosperity and societal green ambitions. We develop a novel data-driven platform to predict and evaluate energy transition policies by applying an artificial neural network and a technology diffusion model. Using Singapore, London, and California as case studies of metropolitan regions at distinctive stages of energy transition, we show that in addition to forecasting renewable energy generation and capacity, the platform is particularly powerful in formulating future policy scenarios. We recommend global application of the proposed methodology to future sustainable energy transition in smart regions.

The paper contains the online supplementary materials for "Data-Driven Prediction and Evaluation on Future Impact of Energy Transition Policies in Smart Regions". We review the renewable energy development and policies in the three metropolitan cities/regions over recent decades. Depending on the geographic variations in the types and quantities of renewable energy resources and the levels of policymakers' commitment to carbon neutrality, we classify Singapore, London, and California as case studies at the primary, intermediate, and advanced stages of the renewable energy transition, respectively.

This study documents that over 10% of the presale contracts in the Hong Kong housing market between 1996 and 2014 were rescinded, resulting in a loss of HKD 436.67 million per year. We then investigate potential determinants of contracts rescission from a novel perspective of option theory. We find out-of-the-money presale contracts (with market price being lower than the outstanding payment at settlement) have a 12.2% higher rescission rate. The rescission rate is also higher when presale homebuyers bear more of the price risk as proxied by option delta and time-induced risk as proxied by time-to-maturity. Moreover, we find rescission rates drop significantly after the Hong Kong government’s housing market macroprudential measures. Our findings shed light on understanding the mechanism of presale contracts rescission, homebuyers’ strategic default behaviour, and the role of housing market regulation in mitigating rescissions.

This study investigates how anti-speculation policies targeting certain real estate submarkets trigger short-term speculators (flippers) to enter non-target submarkets and the impacts of such cross market spillovers on market price and volatility, using complete property transactions in Hong Kong from 1990 to 2020. After the anti-speculation policy that only targeted the presale residential property market took effect in 1994, flippers flowed into the spot residential property market, causing the share of flipping transactions in the spot residential market to increase by 30.7% in 2 years. This spillover significantly reduces the price volatility in the spot residential market by 13.51%, but its impact on price growth is small, implying the price stabilizing effect of flippers. Meanwhile, no spillover into non-residential property markets was found. We also examine an other anti-speculation policy concerning the entire residential property market in 2010. Again, we find no strong evidence that the cross-market spillover continued in non-residential property markets. Our findings emphasize the importance of considering cross-market spillovers in regulating short-term speculations in real estate markets.

The ongoing COVID-19 pandemic has left a strong imprint on many aspects of urban life. Gated communities (GCs) in China are less commonly perceived as a negative and segregated urban form of community compared to other contexts, owing to their wide variety and relative openness. Yet, the enhanced security zone function and the popularity of GCs, along with the heightened segregation and exclusion effects, mean they are most likely to emerge in post-pandemic urban China because of the perceived effectiveness of GCs in preventing health risks by excluding outsiders during the pandemic. Drawing on empirical data from Beijing, this research presents strong evidence for a strengthened perceived ‘security zone’ effect of GCs during the pandemic. Given that rigid pandemic control measures were organized at the community level, a large-scale household survey in Beijing suggests that residents commonly recognise the effectiveness of GCs in security control and show a strong preference for GCs over open communities after the pandemic, even though there is a lack of direct evidence of reduced COVID-19 risk in GCs. The heightened perceived ‘security zone’ function of GCs has shown a significant impact on the housing market, evidenced by an increase of 2% in the housing prices for GCs, compared with those of open communities. The rising popularity of GCs is also evidenced by a significant increase in property viewings by potential homebuyers and smaller price discounts in actual transactions in gated communities vis-à-vis open communities. We argue that the rising risk-averse sentiment in the post-pandemic era has given rise to the popularity of GCs. This study provides timely and fresh insights into the changing meaning of GCs in post-pandemic China.

Mass customization aims to provide goods and services that meet each individual customer's needs with a level of efficiency close to that of mass production. It is also a viable smart manufacturing strategy for companies that want to gain a competitive advantage in the current business environment. Product configurators are one of the major toolkits enabling mass customization. Existing product configurators require customers to choose from a set of predefined attributes or a list of component alternatives. However, customers may feel confused when configuring products if they do not have the necessary domain knowledge about the product. This article proposes a needs-based configurator mechanism that takes customer needs expressed in natural language as input to generate satisfactory product variants as output. This method leverages online product review data to distill the knowledge of customer preferences and needs, which then maps onto the product attribute specifications. A hierarchical attention network is applied to fully extract the information in the review text, which emphasizes the important keywords and phrases. We have obtained the promising experimental results, and our proposed needs-based configurators could help customers to find satisfactory product configurations with high recall rates.

Expected losses anchored to purchase prices can affect actual transactions in different property sectors. Utilizing the data of over a million commercial and residential property transactions in Hong Kong from 1991 to 2015, we find that sellers facing nominal losses relative to their prior purchase prices attained higher selling prices than their counterparts. We suggest two market factors to account for the extent of the loss effect on the market transaction prices. First, the loss effect is only prominent when comparable transaction information is not readily accessible, such as in the less-transacted commercial property market. Second, our results suggest the relevance of the loss effect to the boom-bust property cycle in both the residential and commercial markets. The effect of expected losses on transaction prices is relatively weak in the bust period between 1998 and 2003 when the Hong Kong property market lost almost two-thirds of its value, and it enlarges with the market recovering. The loss effect is not attenuated at the aggregate market level but is associated with strong reductions in price declines in the bust period and in the commercial market. These results have implications for understanding the market adjustment of the loss effect in the property market and its association with the aggregate market dynamics in a boom-bust property cycle.

This paper investigates the effect of a new bus route on noise complaints of residents and the influence of noise on housing price. To overcome the challenge of mapping noise data with subjective emotion, we use a novel data source-text-based noise complaint records from residents in a town in Singapore-and apply natural language processing tools to conduct sentiment analysis. To address the endogeneity concern regarding the bus route, we use a hypothetical least cost path as an instrument for the existing bus route. We find that living closer to the bus route for every 100 m increases noise complaints by around 10 percentage points, and the effect is more severe on medium floor levels (5th-8th floors) and near bus stops (within 100 m). We further link noise with housing price and discover a price reduction of 3% with a 1-scale-point increase in noise complaints. This implies that bus noise offsets 17.8% of the benefit from convenience, which sheds light on the importance of noise insulation in transit-oriented developments.

A rapidly expanding universe of technology-focused startups is trying to change and improve the way real estate markets operate. The undisputed predictive power of machine learning (ML) models often plays a crucial role in the 'disruption' of traditional processes. However, an accountability gap prevails: How do the models arrive at their predictions? Do they do what we hope they do—or are corners cut?Training ML models is a software development process at heart. We suggest to follow a dedicated software testing framework and to verify that the ML model performs as intended. Illustratively, we augment two ML image classifiers with a system testing procedure based on local interpretable model-agnostic explanation (LIME) techniques. Analyzing the classifications sheds light on some of the factors that determine the behaviour of the systems.

This study investigates impact of social integration on migrants’ economic behaviours from a new temporal perspective. We use Singapore’s unique differential public housing policies as a quasi-natural experiment, conducted a household survey on social integration among 1,128 migrants and local households living in public housing estates, and linked them with their nearest housing transaction records. In public open rental housing market, in which Singapore migrants in early post-migration years are permitted to reside, migrant renters select housing in areas up to 3.04% farther from their workplace with 1 more year of residency, physically and spatially making their way into the host society. Migrant renters also pay rents that are lower by up to 0.67% with 1 more year of post-migration residency. However, in public owner-occupied housing market, in which migrants are allowed to purchase their home after obtaining citizenship, there are no differences between local-born and converted-citizen homebuyers. Our results emphasize importance of integration policy at early stages of migration.

Hong Kong introduced a Tobin property tax—the Special Stamp Duty (SSD) Policy—in 2010, which substantially increased the selling costs of short-term property holders. This study examines the effectiveness of this Tobin property tax in curbing speculation and cooling down the market. We find that SSD effectively curtails short-term speculations and reduces flippers’ (holding period less than 2 years) market presence, which fell from 23.2% in 2009 to 2.4% in 2011 and 0.9% in 2013. However, 1 year after implementing the tax, the housing price shows an upward trend of 12.64% and 15.76% in the primary and secondary markets, respectively, indicating a lack of a market cooling effect. We show that flippers strategically defer sales to circumvent SSD charges, resulting in the sharp bunching of urgent sales immediately after the lock-in period ends. Further, SSD effectively increases selling costs and prolongs potential sellers’ holding periods, thereby significantly reducing liquidity and driving up prices in the secondary market. We also document an unintended externality on market dynamics: the unmet housing demand from the secondary market triggers a buying frenzy into the primary market, which increases the prices in both markets. Our findings have policy implications for the viability of Tobin taxes for regulating real estate markets.

We investigate the impact of headquarters relocation on the local economy by examining its spillover effects on the housing market. We find that headquarters relocation into a district leads to 10% higher housing price growth in the district. Moreover, we document a temporal spillover effect as housing prices increase one year before the relocation and rise further until two years afterwards, and a spatial spillover effect on nearby districts’ housing markets of up to 15 miles. We further find agglomeration economies exacerbate the effect of corporate relocation on the housing market.