The resilience and realignment of house prices in the era of Covid-19*

Purpose The study aims to analyze the effects of the Covid-19 pandemic on house prices. Design/methodology/approach The authors start by discussing the possibility that house price indexes may not fully incorporate the effects of the pandemic as of yet. Against the background of the pandemic, the authors then analyze economic and behavioral effects affecting house prices. The authors also discuss how the linkages between tourism and house prices have been affected. The authors further present evidence of an emerging shift in preferences from urban locations to more peripheral ones. Findings The authors report variance in the evolution of house prices across countries at the onset of the pandemic, with locations depending heavily on tourism showing slower price appreciation while appreciation has firmed in other places. The authors argue that the resilience of house prices is not only because of the low-interest rate environment and government efforts to support firms and households, but also behavioral factors. In some locations, the price of condominiums has declined relative to the price of detached houses. This could indicate that wealthier households are seeking more space and larger units as a result of the crisis. There is also evidence of a downward pressure on rents, leading to increased price–rent ratios in the USA. Originality/value By considering both economic and behavioral factors, this paper provides for a better understanding of the resilience and realignment of house prices at the onset of the Covid-19 pandemic.

INTERNAL AND EXTERNAL DETERMINANTS OF HOUSING PRICE BOOM IN HONG KONG

Hong Kong’s housing market witnessed a dramatic housing price appreciation in recent years, with the price index for private domestic housing units being three times higher than ten years ago. This trend is supported by both internal and external factors, as illustrated in this paper. By developing a theoretical model and an empirical analysis on the key variables influencing housing prices using monthly data from 1999 to 2018, we find that the main drivers of housing price appreciation are from the demand side and include income level, money supply and inflation. The main contribution of this study is the quantification of the role of Mainland China’s macroeconomic factors in housing price booms in Hong Kong. Our study shows that capital inflow from and inflation and recessions in Mainland China contribute to increasing housing prices in Hong Kong because the city’s real estate is seen as a way to preserve asset value. These findings call for the need for control of capital inflow between the two economies as well as for stricter regulations against empty houses in Hong Kong.

Economic Consequences of Housing Speculation

By exploiting variation in state capital gains taxation as an instrument, we analyze the economic consequences of housing speculation during the U.S. housing boom in the 2000s. We find that housing speculation, anchored, in part, on extrapolation of past housing price changes, led not only to greater price appreciation, economic expansions, and housing construction during the boom in 2004–2006 but also to more severe economic downturns during the subsequent bust in 2007–2009. Our analysis supports supply overhang and local household demand as two key channels for transmitting these adverse effects.

Tax-Advantaged Trust Use Among IPO Executives: Determinants and Implications for Valuation and Future Performance

ABSTRACT We examine the prevalence and determinants of CEOs' use of tax-advantaged trusts prior to their firm's IPO. Twenty-three percent of CEOs use tax-advantaged pre-IPO trusts, and share transfers into tax-advantaged trusts are positively associated with CEO equity wealth, estate taxes, and dynastic preferences. We project that pre-IPO trust use increases CEOs' dynastic wealth by approximately $830,000, on average. We next examine a simple model's prediction that trust use will be positively related to IPO-period stock price appreciation. We find that trust use is associated with 12 percent higher one-year post-IPO returns, but is not significantly related to the IPO's valuation, filing price revision, or underpricing. This evidence is consistent with CEOs' personal finance decisions prior to the IPO containing value-relevant information that is not immediately incorporated into market prices. JEL Classifications: D14; G12; G32; M21; M41. Data Availability: Data are available from the public sources cited in the text.

Spatial analysis of Twitter sentiment and district-level housing prices

Purpose Studies have shown a correlation and predictive impact of sentiment on asset prices, including Twitter sentiment on markets and individual stocks. This paper aims to determine whether there exists such a correlation between Twitter sentiment and property prices. Design/methodology/approach The authors construct district-level sentiment indices for every district of Istanbul using a dictionary-based polarity scoring method applied to a data set of 1.7 million original tweets that mention one or more of those districts. The authors apply a spatial lag model to estimate the relationship between Twitter sentiment regarding a district and housing prices or housing price appreciation in that district. Findings The findings indicate a significant but negative correlation between Twitter sentiment and property prices and price appreciation. However, the percentage of check-in tweets is found to be positively correlated with prices and price appreciation. Research limitations/implications The analysis is cross-sectional, and therefore, unable to answer the question of whether Twitter can Granger-cause changes in housing markets. Future research should focus on creation of a property-focused lexicon and panel analysis over a longer time horizon. Practical implications The findings suggest a role for Twitter-derived sentiment in predictive models for local variation in property prices as it can be observed in real time. Originality/value This is the first study to analyze the link between sentiment measures derived from Twitter, rather than surveys or news media, on property prices.