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People are starting to doubt business recovery in office market after global financial crisis in 2008. And most institutional and individual portfolios are undiversified in real estate : incurring enormous transaction costs than other financial products. Especially office market has more risks than other real estate market for high transaction costs and many related industries. Although this stuff can lead to increase the risk of investment in office market, nor are there markets that would allow individuals and institutions to hedge their risks. The establishment of office price index future and option markets is likely to hedge away large risk in office market, and provides lowering transaction costs for trading in real estate. We estimate Office Price Index using S&P/Case-Shiller repeat sale estimator which is proposed by Robert. J. Shiller in 1991. The estimator is a sort of Feasible Generalized Two Stage Least Squares, which compute Value weighted Arithmetic Price Index.

The Mexican housing industry was hindered by a shrinking market and tighter financial conditions related to the Great Recession. Moreover, in 2013, a major change in public policy further modified this industry’s environment. Mexico’s new urban development policy supported inner-city new housing, in contrast to the previous policy that incentivized sprawling. Three out of eight publicly traded housing companies filed for bankruptcy protection in 2013-2014, arguably because of the effects of the Great Recession and the new housing policy. The purpose of this study is to identify firm-level factors that caused some firms to file for bankruptcy protection.

Three approaches were used to analyze the housing industry in Mexico from 2006 to 2015. First, a policy analysis focused on the new housing policy and its consequences for housebuilding companies. Second, a financial analysis of the two economic shocks was performed in search for the transmission mechanisms in the companies’ financial metrics. Third, a retrospective analysis using the Fisher’s exact test was used to identify variables statistically associated with companies filing for bankruptcy protection.

There are two features significantly associated with bankruptcy protection: increasing market share while being vertically integrated, as a response to the Great Recession, and the relative magnitude of the loss on firms’ inventory value due to the new public policy. Neither Altman’s Z-score values nor firm size or degree of integration are significantly related to bankruptcy.

The small sample size presented a challenge, as most statistical methodologies require large samples; however, this was overcome by using the Fisher’s exact test.

The main contribution of this paper is the statistical identification of the possible causes for bankruptcy protection in Mexico amongst homebuilding firms in 2013 and 2014, which have not previously been reported in the literature.

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.

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.

This paper aims at analyzing the asymmetries created by the Great Recession in the US real estate sector.

This paper uses a Markov-switching dynamic regression model in which parameters change when the housing market moves from one regime to the other.

The results show that the effect of real estate loans, interest rate, quantitative easing and working age population are asymmetric across bull and bear regimes. It is also found that the estimated parameters are larger in bull regime than bear regime, indicating a tendency to create house price bubbles in bull market.

Since three of those asymmetric variables (real estate loans, interest rate and quantitative easing) are related to monetary policy, the Fed can mitigate their impact on an interest-sensitive sector such as housing by engaging in a countercyclical monetary policy.

The estimated intercept and the variance parameter both vary from one regime to the other, thus justifying the use of a regime-dependent model.

The purpose of this exploratory paper is to examine the lack of reliability of traditional neo-classical models and to argue that it is due to the hidden complexity and non-linearity that may operate at times in residential housing markets. As a result, market efficiency may be a special case, rather than the prevailing rule. An alternative framework that incorporates the higher order concepts of complexity – based on the non-linear, emergent behavior of multiple agents – is required to model discontinuities and imbalances in the housing markets.

The paper examines the building block concepts required to model the complexity of the housing market and analyzes their implications. These implications can be counter-intuitive and help explain the failure of policy makers to model the recent bust in global housing markets.

The paper finds that policy makers need to adopt an analytical framework that incorporates non-linearity, emergence and other building blocks of complexity in order to construct representative financial models that help understand systemic imbalances that may afflict residential housing markets.

This is the first paper to one's knowledge that argues that policy makers should adopt an alternative theoretical framework based on complexity concepts in order to create more effective financial models; such models should include indicators that provide early warning signals of potential discontinuities in housing markets.

The purpose of this paper is to study performance and market timing ability of equity real estate investment trusts (REITs).

The authors use classical regression‐based framework and their multi‐index, multifactor, and conditional extensions to jointly detect asset selectivity and market timing ability of equity REITs and their subcategories. These results are then validated by a nonparametric test.

It is found that equity REITs in aggregate have some housing market timing ability. Various equity REIT subcategories perform differently: office REITs can discover underpriced properties, while retail, industrial, and office REITs have poor timing ability. Nonparametric tests confirm that equity REITs do not have ability to predict real estate market movements.

Research in REIT performance evaluation is still limited to the asset selectivity aspect. This paper intends to fill this gap by providing empirical evidence of market timing ability of equity REITs using an array of parametric and nonparametric methods.

The purpose of this paper is to investigate linkages between households’ expectations and credit markets in the housing crisis.

In the Markov-switching framework, the sample period is classified into high- and low-impact regimes based on impacts of expectations on default rates, and the good-time-to-buy (GTTB) index is chosen to proxy for expectations toward the housing-market dynamics.

The results suggest that in high-impact regimes, optimistic expectations are substantially associated with lower defaults for all default rates analyzed, and second mortgage defaults are more sensitive to households’ expectations than first mortgage defaults. In low-impact regimes, the GTTB index significantly influences composite and first-mortgage default rates, but its impact is insignificant for second mortgage and bankcard default rates.

The results provide compelling evidence that households’ expectations play more important roles in credit markets in turmoil periods.

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 the study is to examine the long-run convergence properties of condominium prices based on the ripple effect for five major US metropolitan areas (Boston, Chicago, Los Angeles, New York and San Francisco). Specifically, we test for both overall convergence in condominium prices and the possibility of distinct convergence clubs to ascertain the interdependence of geographically dispersed metropolitan condominium markets.

Our analysis uses two approaches to identify the convergence properties of condominium prices: the Lee and Strazicich (2003) unit root test with endogenous structural breaks and the Phillips and Sul (2007, 2009) time-varying nonlinear club convergence tests.

The Lee and Strazicich (2003) unit root tests identify two structural breaks in 2006 and 2008 with the rejection of the null hypothesis of a unit root and long-run convergence in condominium prices in the cases of Boston and New York. The Phillips and Sul (2007, 2009) club convergence test reveals the absence of overall convergence in condominium prices across all metropolitan areas, but the emergence of two distinct convergence clubs with clear geographical segmentation: on the east coast with Boston and New York and the west coast with Los Angeles and San Francisco while Chicago exhibits a non-converging path.

The results highlight the distinct geographical segmentation of metropolitan condominium markets, which provides useful information to local policymakers, financial institutions, real estate developers and real estate portfolio managers. The limitations of the research are the identification of the underlying sources for the convergence clubs identified due to the availability of monthly data for a number of potential variables.

The absence of overall convergence in condominium prices, but the emergence of distinct convergence clubs that reflects the geographical segmentation of metropolitan condominium markets raises the potential for portfolio diversification.

Unlike previous studies that have focused on single-family housing, this is the first study to examine the convergence of metropolitan area condominium prices.