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This study aims to analyze the impact of technology-based corporation relocation on housing price indices during COVID-19 within the metropolitan areas of Austin, Texas and Seattle/Bellevue, Washington.The corporations under observation were Tesla and Amazon, respectively. The analysis intends to understand economic drivers behind the housing market and the radius of its effect while including fixed and random effects.

This study used a difference-in-difference (DID) method to evaluate changes in housing price index near and further away from Tesla’s and Amazon’s new corporate locations. The DID method allows for the capture of unique regional characteristics, as it requires a treatment and control group: housing price index and 5-mile and 10-mile search radii centered from the new corporate location.

The results indicated that corporate relocation announcements had a positive effect on housing price index post-pandemic. Specifically, the effect of Tesla’s relocation in Austin on the housing price index was not concentrated near the relocation site, but beyond the 5- and 10-mile radii. For Seattle/Bellevue, the effect of Amazon’s relocation announcement on housing price index was concentrated near the relocation site as well as beyond a 10-mile radius. Interestingly, these findings suggest housing markets incorporate speculation of prospective economic expansion linked with a corporate relocation.

Previous literature assessed COVID-19 housing market conditions and the economic effects of corporate relocation separately, whereas this study analyzed the housing price effects of corporate relocation during COVID-19. The DID method includes spatial and temporal analyses that allow for the impact of housing price to be observed across specified radii rather than a city-wide impact analysis.

The purpose of this study is to determine whether house prices and income share a stable, stationary relationship in the G-7 countries. This stable relationship has been clearly implied by theory but has been difficult to uncover empirically in previous studies.

The analysis entails using nonlinear tests for a stationary relationship between home prices and per-capita income for the G-7 countries, whereas most previous papers on the topic have used linear methods.

When the standard linear ADF test is used, no stationary relationship for home prices and income is found for any of the G-7 countries. When the more powerful (but still linear) Ng–Perron test is used, the USA, but no other G-7 country, exhibits a stable relationship between the two variables. When the nonlinear Enders–Granger test is used, stationarity between home prices and income is found for five of the remaining six G-7 states.

Previous research has shown that as house prices have risen far above the income, especially over bubble periods, income has done a poor job in predicting home values. The findings show that income has a clear long-run stationary relationship with home values. This implies income could be helpful in providing home price forecasts.

Where previous studies have failed to find a long-run relationship between home prices and income while using linear methods, results in this paper show this theoretical asset–pricing relationship holds once the adjustment process is allowed to exhibit nonlinearity.

This study aims to examine the permit changes enacted by the city of Portland, Oregon, USA, on the construction and subsequent short-term rental of tiny homes. The permitting process was eased by the city in 2014. The city’s enforcement of occupancy and rental ordinances, sometimes called Airbnb laws, were tightened in 2019. The new code restrictions are tighter than the rental codes that existed previously.

This paper uses time-series data to first consider the thesis that relaxing building permit requirements for tiny homes has encouraged legal construction and increased the number of applications filed with the city planning office. The number of permits was the dependent variable and time-sensitive dummy variable was the independent variable. An adjusted T-statistic was calculated using a least-squares regression model with a moving average autocorrelation adjustment. The second regression model considers the financial relationship between active listings on Airbnb and HomeAway to a housing price coverage ratio and the aggregated dynamic-factor model used to calculate the economic activity index for Portland.

There were two reported case study findings. The first regression used a dummy variable measuring the application response to permit easing. It was positive and significant. The second finding measures active host listings on Airbnb whether they are directly associated with the calculated multiple of the changes in the S&P/Case–Shiller housing price index low tier divided by weekly employee income. Higher numbers for this coverage ratio suggest that listings on short-term rental platforms are increasing directly with the ratio. The economic activity index is insignificant when predicting the level of listings. Regression results indicate that property owners are financially motivated to list dwellings as visitor rentals and possibly motivated to install tiny homes behind their primary residences as short-term rental units. Local economic conditions do not seem to influence the number of properties listed on short-term rental websites.

Higher coverage ratios encourage property owners to list dwellings on short-term rental websites in the absence of enforceable rental restrictions. Without a method to quickly and feasible identify owners violating short-term rental restriction legislation and enforce fines there is a tendency for active listings to grow in a locale. San Francisco, California, under its new short-term rental ordinance requires online websites such as Airbnb to enforce permit requirements. San Francisco’s ordinance change seems to have resulted in a dramatic drop in active listings available for visitor rentals.

Information published by Inside Airbnb and Airdna does not separate entire dwelling information into categories such as single-family detached houses; tiny homes; apartments; or condominiums ownership types. Even public housing units are sometimes listed as short-term rentals. The aggregate data makes the relationship between active listings and the coverage ratio difficult to interpret. Listing information is limited and only available for a three-year rolling cycle on a quarterly basis for the city of Portland, Oregon.

Future research studies could consider how tiny homes might play a role in providing permanent housing to local residents or for providing a shelter for the homeless in cities experiencing acute long-term rental shortages. Does limiting the number of homes available as short-term visitor rentals noticeably increase the quantity of housing and lower the monthly rental rates available to permanent residents of the city? Cities have passed short-term rental codes with the objective of increasing the availability of rental housing available to residents at affordable prices.

Prior research studies focused on who purchases tiny homes; tiny homes used as housing for the homeless; communities composed of tiny homes; and the connection between tiny home living and political activism. The study herein links permit changes to tiny-home building applications. It uses the home price index low tier and the economic condition index for the Portland metropolitan area to predict the number of active listings on Airbnb and HomeAway websites pre-regulation enforcement.

Recent research has found significant relationships between internet search volume and real estate markets. This paper aims to examine whether Google search volume data can serve as a leading sentiment indicator and are able to predict turning points in the US housing market. One of the main objectives is to find a model based on internet search interest that generates reliable real-time forecasts.

Starting from seven individual real-estate-related Google search volume indices, a multivariate probit model is derived by following a selection procedure. The best model is then tested for its in- and out-of-sample forecasting ability.

The results show that the model predicts the direction of monthly price changes correctly, with over 89 per cent in-sample and just above 88 per cent in one to four-month out-of-sample forecasts. The out-of-sample tests demonstrate that although the Google model is not always accurate in terms of timing, the signals are always correct when it comes to foreseeing an upcoming turning point. Thus, as signals are generated up to six months early, it functions as a satisfactory and timely indicator of future house price changes.

The results suggest that Google data can serve as an early market indicator and that the application of this data set in binary forecasting models can produce useful predictions of changes in upward and downward movements of US house prices, as measured by the Case–Shiller 20-City House Price Index. This implies that real estate forecasters, economists and policymakers should consider incorporating this free and very current data set into their market forecasts or when performing plausibility checks for future investment decisions.

This is the first paper to apply Google search query data as a sentiment indicator in binary forecasting models to predict turning points in the housing market.

This chapter investigates a shock transmission path between a home country (a country where globalized banks’ headquarters are located) and a host country (Indonesia as the emerging market) through the lending channel of global banks’ local branches (i.e., the internal transfer channel). Using novel data of monthly individual foreign bank’s balance sheet in Indonesia, the author finds the evidence that shocks to a parent bank and a home economy are transmitted to a host economy through the foreign banks’ internal capital market. With the Indonesia banks’ capital injections and their difficulty in financing dollar funds without risk premiums since the 1998s crisis, the foreign banks’ dollar lending in Indonesia is a good showcase of internal capital markets. A change in a home stock market index and industrial production appears to have a negative effect on growth rates in foreign currency loans of foreign banks in the host market. On the other hand, high growth rates in the parent bank’s stock price in the home market lead to an increase in foreign banks’ US dollar lending in the host country. This effect does not appear in local currency lending because limited hedging instruments against foreign exchange risk results in immobility of bank capital in the local currency.

The purpose of this paper is to outline a new approach to risk management that will create an innovative marketplace mechanism to deal with risk.

The paper discusses current risk management practice and proposes a novel new approach to risk management with an example.

This paper proposes the creation of a restructured mortgage product to transfer the home price volatility risk explicitly to investors and portfolio managers.

This paper proposes product innovation to transfer risk. The idea is original and is a conceptual viewpoint aimed at urging the industry to implement the concept. The recent credit crisis highlights the problem of unhedged home price volatility born by individual borrowers. As borrowers are not equipped to hedge this risk, it is important to restructure the mortgage to explicitly transfer the risk of home price volatility to investors and mortgage lenders.

The purpose of this paper is to create an endurance index of housing investor sentiment and use it to forecast housing stock returns. This study performs not only in-sample and out-of-sample forecasting, like many previous studies did, but also a true forecasting by using all lag terms of independent variables. In addition, an evaluation procedure is applied to quantify the quality of forecasts.

Using a binomial probability distribution model, this paper creates an endurance index of housing investor sentiment. The index reflects the probability of the high or low stock price being the close price for the Philadelphia Stock Exchange Housing Sector Index. This housing investor sentiment endurance index directly uses housing stock price differentials to measure housing investor reactions to all relevant news. Empirical results in this study suggest that the index can not only play a significant role in explaining variations in housing stock returns but also have decent forecasting ability.

Results of this study reveal the considerable forecasting ability of the index. Monthly forecasts of housing stock returns have an overall accuracy of 51 per cent, while the overall accuracy of 8-quarter rolling forecasts even reaches 84 per cent. In addition, the index has decent forecasting ability on changes in housing prices as suggested by the strong evidence of one-direction causal relations running from the endurance index to housing prices. However, extreme volatility of housing stock returns may impair the forecasting quality.

The endurance index of housing investor sentiment is easy to construct and use for forecasting housing stock returns. The demonstrated predictability of the index on housing stock returns in this study can have broad implications on housing-related business practices through providing an effective forecasting tool to investors and analysts of housing stocks, as well as housing policy-makers.

Despite different investor sentiment proxies suggested in the previous studies, few of them can effectively predict stock returns, due to some embedded limitations. Many increases and decreases inn prices cancel out each other during the trading day, as many unreliable sentiments cancel out each other. This dynamic process reveals not only investor sentiment but also resilience or endurance of sentiment. It is only long-lasting resilient sentiment that can be built in the closing price. It means that the only feasible way to use investor sentiment contained in stock prices to forecast future stock prices is to detach resilient investor sentiment from stock prices and construct an index of endurance of investor sentiment.

This paper aims to compare a median‐to‐median estimate of residential price change to the FHFA home price index composed of repeat sales. Further, it seeks to use the complete population of closed sales. The conceptual issue is the use of a “typical” or average comparison through time as opposed to properties where attributes and their marginal prices are held constant.

The paper uses the total population of closed sales rather than a sample. A time series for median price changes is compared to the FHFA time series. The medians for the complete population are the benchmarks, as the median parameter is the true value.

The study finds that the quarterly FHFA price changes do not capture market movements following a major external shock such as a tropical storm. Further, the FHFA data originate in mortgage applications which are not characteristic of the local market. The conclusion is that a median comparison is better, all deed recordings should be used, and repeat sales are not always a valid tool.

Acquiring the data set of all deed transactions involved a budget which may be available to all analysts.

The practical applications are enormous. The results cast doubts on the FHFA home price index and the Case Shiller index. The paper supports the method used by the National Association of Realtors.

All researchers interested in local real estate markets are concerned about the best method to measure changes in local demand and supply market conditions. This project presents a method to use that is sound conceptually and statistically. The reason is that the goal is to measure changes in the “typical”, or average, property over time.

The literature abounds with repeat sale papers. This paper gives an alternative and avoids the many flaws with paired sales. It should have a wide readership.

This paper, a case study, aims to consider whether the income ratio and rental ratio tracks the formation of residential housing price spikes and their collapse. The ratios are measuring the risk associated with house price stability. They may signal whether a real estate investor should consider purchasing real property, continue holding it or consider selling it. The Federal Reserve Bank of Dallas (Dallas Fed) calculates and publishes income ratios for Organization for Economic Cooperation and Development countries to measure “irrational exuberance,” which is a measure of housing price risk for a given country's housing market. The USA is a member of the organization. The income ratio idea is being repurposed to act as a buy/sell signal for real estate investors.

The income ratio calculated by the Dallas Fed and this case study's ratio were date-stamped and graphed to determine whether the 2006–2008 housing “bubble and burst” could be visually detected. An ordinary least squares regression with the data transformed into logs and a regression with structural data breaks for the years 1990 through 2019 were modeled using the independent variables income ratio, rent ratio and the University of Michigan Consumer Sentiment Index. The descriptive statistics show a gradual increase in the ratios prior to exposure to an unexpected, exogenous financial shock, which took several months to grow and collapse. The regression analysis with breaks indicates that the income ratio can predict changes in housing prices using a lead of 2 months.

The gradual increases in the ratios with predetermine limits set by the real estate investor may trigger a sell decision when a specified rate is reached for the ratios even when housing prices are still rising. The independent variables were significant, but the rent ratio had the correct sign only with the regression with time breaks model was used. The housing spike using the Dallas Fed's income ratio and this study's income ratio indicated that the housing boom and collapse occurred rapidly. The boom does not appear to be a continuous housing price increase followed by a sudden price drop when ratio analysis is used. The income ratio is significant through time, but the rental ratio and Consumer Sentiment Index are insignificant for multiple-time breaks.

Investors should consider the relative prices of residential housing in a neighborhood when purchasing a property coupled with income and rental ratio trends that are taking place in the local market. High relative income ratios may signal that when an unexpected adverse event occurs the housing market may enter a state of crisis. The relative housing prices to income ratio indicates there is rising housing price stability risk. Aggregate data for the country are used, whereas real estate prices are also significantly impacted by local conditions.

Ratio trends might enable real estate investors and homeowners to determine when to sell real estate investments prior to a price collapse and preserve wealth, which would otherwise result in the loss of equity. Higher exuberance ratios should result in an increase in the discount rate, which results in lower valuations as measured by the formula net operating income dividend by the discount rate. It can also signal when to start reinvesting in real estate, because real estate prices are rising, and the ratios are relative low compared to income.

The graphical descriptive depictions seem to suggest that government intervention into the housing market while a spike is forming may not be possible due to the speed with which a spike forms and collapses. Expected income declines would cause the income ratios to change and signal that housing prices will start declining. Both the income and rental ratios in the US housing market have continued to increase since 2008.

A consumer sentiment variable was added to the analysis. Prior researchers have suggested adding a consumer sentiment explanatory variable to the model. The results generated for this variable were counterintuitive. The Federal Housing Finance Agency (FHFA) price index results signaled a change during a different year than when the S&P/Case–Shiller Home Price Index is used. Many prior studies used the FHFA price index. They emphasized regulatory issues associated with changing exuberance ratio levels. This case study applies these ideas to measure relative increases in risk, which should impact the discount rate used to estimate the intrinsic value of a residential property.

The 2007–2009 financial crisis initially appeared to have destroyed a huge amount of wealth in the United States. Housing prices dropped about 21% across the country and as much as 50% in some places, and the stock market dropped by nearly 50% as well. This chapter examines how the financial crisis differentially affected households at different parts of the income and wealth distributions. Our results show that all households lost about the same percentage of their wealth in that period. But because households in the top 10% of the wealth distribution owned many different kinds of assets, their wealth soon recovered. The bottom 80% of the wealth distribution had more of their wealth tied up in housing. We show that financial distress, indexed by foreclosures, being behind in mortgage payments, and changes in house prices were particularly concentrated in households in the bottom 80% of the wealth distribution. These households lost a large part of their wealth and have not yet recovered. Households that were most deeply affected were those who entered the housing market late and took out subprime loans. African American and Hispanic households were particularly susceptible as they bought houses late in the price bubble often with subprime loans.