Housing market forecasts via stock market indicators

Through the reinterpretation of housing data as candlesticks, we extend Nature Scientific Reports article by Liang and Unwin [LU22] on stock market indicators for COVID-19 data, and utilize some of the most prominent technical indicators from the stock market to estimate future changes in the housing market, comparing the findings to those one would obtain from studying real estate ETF's. By providing an analysis of MACD, RSI, and Candlestick indicators (Bullish Engulfing, Bearish Engulfing, Hanging Man, and Hammer), we exhibit their statistical significance in making predictions for USA data sets (using Zillow Housing data) and also consider their applications within three different scenarios: a stable housing market, a volatile housing market, and a saturated market. In particular, we show that bearish indicators have a much higher statistical significance then bullish indicators, and we further illustrate how in less stable or more populated countries, bearish trends are only slightly more statistically present compared to bullish trends.

Behavioral changes in the housing market before and after the Covid-19 lockdown

We exploit unique Norwegian day-by-day transaction and hour-by-hour bidding logs data in order to examine how market participants reacted to the spreading news of Covid-19 in early March 2020, the lockdown on March 12, and the re-opening on April 20. We observe changes on the date of the lockdown in transaction volumes, sell-prediction spreads, exploitative bidding behavior, and seller confidence. However, when we compare observed price developments with our estimated counter-factual price developments, we find that about half of the total fall in prices had already occurred before the lockdown was implemented. The re-opening completely reverses the lockdown effect on prices. We show that voluntary behavioral changes, as well as lockdown and re-opening effects, are visible in various measures of social mobility, and that changes in daily news sentiment correlate with the abnormal price movements during this period.

Local Housing Market Sentiments and Returns: Evidence from China

This paper examines the impacts of local housing sentiments on the housing price dynamics of China. With a massive second-hand transaction dataset, we construct monthly local housing sentiment indices for 18 major cities in China from January 2016 to October 2020. We create three sentiment proxies representing the local housing market liquidity and speculative behaviors from the transaction dataset and then use partial least squares (PLS) to extract a recursive look-ahead-bias-free local housing sentiment index for each city considered. The local housing sentiments are shown to have robust predictive powers for future housing returns with a salient short-run underreaction and long-run overreaction pattern. Further analysis shows that local housing sentiment impacts are asymmetric, and housing returns in cities with relatively inelastic housing supply are more sensitive to local housing sentiments. We also document a significant feedback effect between housing returns and market sentiments, indicating the existence of a pricing-sentiment spiral which could potentially enhance the ongoing market fever of Chinese housing markets. The main estimation results are robust to alternative sentiment extraction methods and alternative sentiment proxies, and consistent for the sample period before COVID-19.

A view to die for? Housing value, wildfire risk, and environmental amenities

Utilizing a unique database of residential real estate transactions, we examine the price change near the boundary of the Bushfire Prone Area (BPA), a planning zone indicating high wildfire risk, after experiencing a salient wildfire event in Australia from 2015 to 2016. While the properties within the BPA are valued more by 1.6%-1.9% in general, home buyers pay less for the properties in the BPA by 0.9%-1.7% after experiencing a salient wildfire event compared with the properties located outside of the BPA. Moreover, the properties in the BPA with greater environmental amenities are more resilient to the impact of wildfire risk salience than the rest BPA, necessitating an analysis of the role of environmental amenities in the research on wildfire risk in the housing market context.

A choice experiment for testing the energy-efficiency mortgage as a tool for promoting sustainable finance

The challenges currently facing the EU in the energy sector include increasing import dependence, limited diversification, high and volatile energy prices, decarbonization, and slow progress in energy efficiency. EU energy policy has provided a wide range of measures to achieve an integrated energy market and sustainability of the building sector. Various incentives and financial instruments have been promoted and financed by governments to help consumers in energy retrofit processes. These include direct investments and fiscal, financial, and market instruments. Public measures have been widely studied but private initiatives have not. In this study, the energy-efficiency mortgage (EEM) is investigated as a credit scheme to attract young people to a real estate market for sustainable buildings, characterized by high sale prices. A choice experiment (CE) was modelled to investigate the preferences of potential young buyers of a new home. The results of this exploratory survey showed an appreciation of the purchase of new A-rated properties according to the Energy Performance Certificate, compared to those that need to be retrofitted or not retrofitted. Consumers like the option of a home energy efficiency renovation being fully managed by a third party (i.e., following the one-stop shop model). The key appeal of energy efficiency for consumers ranges from its ability to support better energy management, to better property value management. The EEM seems to be a promising tool to stimulate investments in energy efficiency and to promote the accessibility and affordability of housing in the sustainable housing market. The results of this study can help private financial institutions to propose appropriate credit plans, which limit the risk of default by the borrower. They can also help public incentive policymakers to propose complementary instruments to EEM.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12053-022-10035-y.

Identification of house price bubbles using robust methodology: evidence from Polish provincial capitals

This article aims to check whether there has been a price bubble in the Polish major housing markets in recent years. To accomplish this goal, the log price-to-rent ratios in Polish provincial cities were analysed. In order to avoid incorrect conclusions, the log price-to-rent ratio using the instrumental variable estimation and ordinary least squares methods was decomposed into two components: fundamental and non-fundamental. The latter was then examined using the Phillips, Shi, and Yu procedure to detect explosive and downward movements. The results of the study showed that, in general, over 2011, actual log price-to-rent ratios in the analysed cities were below their fundamental values, i.e., a negative price bubble existed. However, more or less since the beginning of 2013, the surveyed markets have seen an increasing level of the non-fundamental component of the index under study, and its particularly explosive movements are visible in the first quarters of 2014. Finally, this analysis indicated future research directions and study implications for Polish policy-makers, housing investors, and households.

Testing the asymmetric causal nexus of housing-oil prices and pandemic uncertainty in four major economies

The spread of COVID-19 worldwide has shown how quick global economy can become affected when ones' health and future are at risk. This paper examines the evidence of Granger causality among the housing price, the unemployment rate, crude oil price, and world pandemic uncertainty in France, Germany, the UK, and the USA over the period 1996Q1-2019Q2. In this case, the linear and asymmetric Granger causality approaches of Toda-Yamamoto and Hatemi-J are respectively applied to provide useful insight. Although only significant evidence of linear Granger causality is found among the unemployment rate and the house prices in all the four economies, the investigations revealed asymmetric evidence involving the world pandemic uncertainty. Specifically, there is a significant uni-directional asymmetric Granger causality from the world pandemic uncertainty to the house price in France, Germany, and the USA but not in the UK. The variation in the results among the examined countries is explained by potential differences in economic structures or business cycle and other social and economic factors. Thus, relevant policy guidance is implied from the results especially for the policymakers in the examined countries.

Online housing search dataset: Information flows of real estate platform users

This data article describes user-generated data of Funda.nl, the largest online housing market website of the Netherlands. The data contain the inflow and outflow of hits (mouse clicks, opening of webpages, etc.) at the municipality level. The municipality of the user defines the origin and the municipality of the property that is viewed defines the destination. The data capture real behavior of the platform users. The flow data are based on 1.1 billion hits that are made by the users of the website in the first six months of 2018. The underlying data are collected by Google Analytics, the web analytics tool of Google. Funda utilizes the data for platform stability, security, product development, etc. The proprietary data of Funda are used to generate the information flows between municipalities. In the full sample we have 148,216 information flows between municipalities in the Netherlands, among which 313 zero flows. The data include subsamples for different types of platform users as user search intentions range from serious to fully recreational. The data enable researchers to analyze housing search behavior from a novel perspective. The data are, for instance, relevant for housing market researchers, digital economists, and economic geographers.

Testing for overall and cluster convergence of housing rents using robust methodology: evidence from Polish provincial capitals

The aim of this paper is to test for overall and cluster convergence of housing rents across Polish provincial capitals and to identify drivers of convergence club formation. In order to achieve the goal of the study, several novel convergence tests were used, including the Kong et al. (J Econom 209:185-207, 2019. https://doi.org/10.1016/j.jeconom.2018.12.022) and Phillips and Sul (Econometrica 75:1771-1855, 2007. https://doi.org/10.1111/j.1468-0262.2007.00811.x) approaches. Moreover, club convergence analysis was carried out in four different configurations, varying in the technique of trend component extraction from the data. In particular, three well-known methods of time series decomposition were used, i.e. the Hodrick-Prescott, Butterworth and Christiano-Fitzgerald filters, as well as the most recent boosted Hodrick-Prescott filter. The results indicated that rental prices across the studied cities do not share a common path in the long run. It is possible, however, to identify convergence clubs where rents are moving towards a club-specific steady state. Detailed analysis of the structure of estimated clusters showed that data filtering using the boosted Hodrick-Prescott method leads to the most reliable allocation of cities to convergence clubs. Moreover, the estimation of logit models revealed that the likelihood of any two cities belonging to the same convergence club depends mainly on similar levels in terms of the unemployment rate, housing stock, city area, and the number of students. Finally, recommendations for local and national policy-makers concerning the development of the rental market have been formulated, particularly in the areas of urban land-use planning policy, housing legislation and public-private partnerships.

Linkage structure of China's housing market and its risk-defusing capability

There have been many recent fears of severe house-price decreases in some provinces in China causing a nationwide collapse of the housing market. Therefore, this paper aims to clarify the linkage structure of China’s housing market and its risk contagion routes. Given monthly data of provincial housing and stock-market capital returns from 2001M01 to 2019M12, on the basis of graph theory, this paper first explores the linkage structure of provincial housing markets. Relying on the linkage structure, this paper then simulates the effect of unexpected negative shocks from the stock market on the probabilities of a housing-market collapse based on the epidemic model. The results show that (i) consistently with practical evidence, the probability of housing-market collapse is relatively high in the southwest of China and (ii) reducing housing-market linkage, such as through a blocking mechanism, to prevent collapse is helpful.

Detection of price bubbles in Istanbul housing market using LSTM autoencoders: a district-based approach

Since the early 2000s, there has been a long-term price increase trend in the Istanbul housing market, and this situation also has led to price bubble speculations. Since the housing sector was caught with a high level of unsold housing stock to the economic slowdown emerging in the second half of 2018, housing price bubble speculations have increased even more, especially for the Istanbul market. In this period, housing loan interest reduction campaigns were implemented by the government through state banks to stimulate the housing demand, and a probable collapse in the housing market was prevented. On the other hand, house prices continued to rise during this period due to the stimulated demand. In this paper, we perform a price bubble research on the selected districts in the Istanbul housing market over the 2007–2019 period using LSTM autoencoder model. The first analysis on monthly data is performed by using housing price index, housing rent index, consumer prices index, stock market index, return on government debt securities, USD/TRY exchange rates, BIST price index, monthly deposit interest rates, mortgage interest rates and consumer confidence index, and the second analysis on quarterly data is carried out by adding building construction cost index and GDP data to the previous dataset. In the first analysis, the bubble formations differ regionally and periodically and disappeared toward the end of 2019 in some districts, while in the second analysis, the housing bubble formations have a more common and continuous appearance. Experimental results show that LSTM autoencoder model can be used to detect housing bubbles effectively.

Housing market bubbles and urban resilience: Applying systems theory

In this paper, we aim to connect the academic discourses of housing market and urban resilience. For this purpose, we discussed the housing market and its performance within the broader housing system, with a specific attention to the transient state of the housing market bubble. Through a systematic literature review, the housing system is framed based on the involved actors and their interactions. This systems theory based view is then used to provide a broader definition of the variations in the market performance within the urban resilience literature. In this sense, the theoretical underpinning of the literature of resilience within the systems and the observed variations in the housing market performance is discussed in view of shocks and stressors to the system, as well as equilibria and responses to disturbances. The aim is to provide a foundation for an alternative theoretical framework that focuses on interactions within the market and can be used to understand the impact of the housing market's financial drivers on urban resilience.

The dynamic nexus of crop production and population growth: housing market sustainability pathway

The urban poor residents in South Africa are over time known for imbalance and inadequate housing amidst recent concern of shock in food production. In studying this peculiar problem, this study investigates the cointegration and long-run equilibrium relationship of population growth, crop production, and the housing price in the country. Empirically, a quarterly data from 1975:Q1 to 2015:Q4 is employed using the conventional Autoregressive Distributed Lag. The investigation shows strong significant evidence of cointegration and a quarterly speed of adjustment of 17.2% to long run in the system. Also, as the population grows, a decline in house price index is experienced in the long run. Although unusual, adequate and sustainable housing plan, demand-supply dynamics, in respect to a country's population expansion could posit observation. But, in the short run, a strongly significant positive association is observed. It shows further that positively short-run and long-run relationships significantly exist between crop production and house price index. In reality, caution is essential in the introduction of land redistribution policy to avoid hampering the housing policies and 2030 housing target of the government.

Home is where the ad is: online interest proxies housing demand

Online activity leaves digital traces of human behavior. In this paper we investigate if online interest can be used as a proxy of housing demand, a key yet so far mostly unobserved feature of housing markets. We analyze data from an Italian website of housing sales advertisements (ads). For each ad, we know the timings at which website users clicked on the ad or used the corresponding contact form. We show that low online interest-a small number of clicks/contacts on the ad relative to other ads in the same neighborhood-predicts longer time on market and higher chance of downward price revisions, and that aggregate online interest is a leading indicator of housing market liquidity and prices. As online interest affects time on market, liquidity and prices in the same way as actual demand, we deduce that it is a good proxy. We then turn to a standard econometric problem: what difference in demand is caused by a difference in price? We use machine learning to identify pairs of duplicate ads, i.e. ads that refer to the same housing unit. Under some caveats, differences in demand between the two ads can only be caused by differences in price. We find that a 1% higher price causes a 0.66% lower number of clicks.

The effect of earthquakes on the housing market and the quality of life in the province of Groningen, the Netherlands

The Netherlands is not known for the occurrence of earthquakes. This is, however, a hot topic in the province of Groningen. Because of gas extraction, this area suffered from more than 1000 earthquakes. Most of them are not very intensive, but also bigger earthquakes of between 2 and 3 on the scale of Richter have been measured. In the last few years, thousands of houses are damaged, house prices have dropped, and the liveability is at stake. This paper pays attention to the effects of these earthquakes on the functioning of the housing market and the quality of life in the region. An extensive survey was conducted, focus groups were organized, housing market statistics were analyzed, and several econometric models were evaluated to give an answer to the question in which way the earthquakes influence the local housing market and the liveability.

Housing price gradient and immigrant population: Data from the Italian real estate market

The database presented here was collected by Antoniucci and Marella to analyze the correlation between the housing price gradient and the immigrant population in Italy during 2016. It may also be useful in other statistical analyses, be they on the real estate market or in another branches of social science. The data sample relates to 112 Italian provincial capitals. It provides accurate information on urban structure, and specifically on urban density. The two most significant variables are original indicators constructed from official data sources: the housing price gradient, or the ratio between average prices in the center and suburbs by city; and building density, which is the average number of housing units per residential building. The housing price gradient is calculated for the two residential sub-markets, new-build and existing units, providing an original and detailed sample of the Italian residential market. Rather than average prices, the housing price gradient helps to identify potential divergences in residential market trends. As well as house prices, two other data clusters are considered: socio-economic variables, which provide a framework of each city, in terms of demographic and economic information; and various data on urban structure, which are rarely included in the same database.

Perceived Housing Discrimination and Self-Reported Health: How Do Neighborhood Features Matter?

BACKGROUND

While the association between perceived discrimination and health has been investigated, little is known about whether and how neighborhood characteristics moderate this association.

PURPOSE

We situate discrimination in the housing context and use relative deprivation and social capital perspectives to fill the knowledge gap.

METHODS

We applied multilevel logistic modeling to 9,842 adults in 830 neighborhoods in Philadelphia to examine three hypotheses.

RESULTS

First, the detrimental effect of discrimination on self-reported health was underestimated without considering neighborhood features as moderators. The estimated coefficient (β) increased from approximately 0.02 to 1.84 or higher. Second, the negative association between discrimination and self-reported health was enhanced when individuals with discrimination experience lived in neighborhoods with higher housing values (β = 0.42). Third, the adverse association of discrimination with self-reported health was attenuated when people reporting discrimination resided in neighborhoods marked by higher income inequality (β = -4.34) and higher concentrations of single-parent households with children (β = -0.03) and minorities (β = -0.01).

CONCLUSIONS

We not only confirmed the moderating roles of neighborhood characteristics, but also suggested that the relative deprivation and social capital perspectives could be used to understand how perceived housing discrimination affects self-reported health via neighborhood factors.

Do natural disasters cause an excessive fear of heights? Evidence from the Wenchuan earthquake

This paper uses the 2008 Wenchuan earthquake in China to examine if the occurrence of a natural disaster can cause an excessive fear of living in upper floors. We rely on potential variations in earthquake risk perceptions by floor level to assess whether the pricing of apartments in lower versus upper floors is consistent with a disproportionate fear of heights. We use a unique transaction dataset for new apartment units in the affected area. We find that the relative price of low to high floor units, particularly units located in the first and second floor, considerably increased for several months after the earthquake and then returned back to the levels observed prior to the tremor. This temporal increase in relative prices is in line with a higher risk perception and fear, triggered after the earthquake, of living in upper floors, which gradually dissipated over time. The results are robust to alternative model estimations.