Search results

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 linkage between bankruptcy filings and hurricane events. Several independent variables related to local district court bankruptcy filings are examined. The primary question posed is whether Category 3,4 and 5 hurricanes result in personal bankruptcy filings due to the real property and other damage that ensures.

Landfall hurricanes in Florida from 2001 through 2018 were examined by using the fully modified least square regression model. Descriptive statistics include elasticity measures that show statistics prior and post the passage of the Bankruptcy Abuse and Prevent and Consumer Protection Act of 2005 (BAPCPA).

The elasticity of housing prices was a useful statistic in explaining bankruptcy filings. Regression results indicate that bankruptcy filing occur within one year of a serious hurricane. The regression model found hurricane events and housing price trends were significant variable when predicting district court bankruptcy filings.

BAPCPA targets fraud under Chapter 7 bankruptcy filings. Unfortunately, this also had the unintended consequence of discouraging legitimated filings due to the lowering of the marginal benefit associated with filing when the “means test” is applied.

Lack of flood insurance coverage and stagnant real estate prices could limit the desirability of filing under Chapter 13 resulting in an inventory of damaged properties being foreclosed.

Prior researchers relied on a descriptive approach by using percentage rates to quantify the association between hurricane damage and bankruptcy filings. By using the fully modified regression-based approach, the study herein establishes that filings occur approximately a year after the household experiences the real property loss and identifies other casual factors that influence the decision to file.

The purpose of this study is to investigate whether increases in homeowner green amenities occurred because of income tax credits to the degree that changes in housing prices are measurable. Are higher incomes, lower mortgage rates and green income-tax credits impacting housing price changes?

The paper uses the least-squares regression model with natural log specifications. The log of income and a dummy variable, which was assigned to the Energy Policy Act (2005) and the American Recovery and Reinvestment Act (2009) coverage dates are used as independent variables. Two regression models were examined using monthly housing price data from January 1990 through the year 2018. The first regression model used a single dummy variable for credits available under the Policy Act of 2005 and the Recovery Act of 2009. The second regression model considered the credits granted under these two laws separately. Disposable income per capita impacts demands for housing while green upgrade expenditures affect the cost of housing.

The laws set low credit limits of $500 followed by $1,500 but because of the multiplier effect, the spending appears to have magnified and been much higher. The credit availability variables have positive coefficients and were significant at 1 per cent. This implies that single-family housing prices were sensitive to the existence of residential energy property income-tax credits. The R² results were 0.93 or above for both models.

The data used was aggregated and publicly available online. Many studies use aggregated macroeconomic data when modeling housing prices using the exogenous variable of disposable income but there is no substitute for examining individual homes by location and their sales price to see under what conditions green income-tax credits have the most impact. There could be demographic issues that are missed when using aggregated information.

Spending on heating/cooling systems, dual pane windows and other green amenities keeps the housing stock modernized and housing prices steady or rising. An additional benefit is that spending motivated by self-interest can simulate household consumption spending. Houses deteriorate due to wear and tear. Physical-repairable depreciation represents a situation where maintenance funds are continuously needing to be spent. Repairs and upgrades to the structure of the property keep its price stable by stopping the physical depreciation that would otherwise occur with the passage of time.

The paper provides support for the idea that residential green amenity upgrades positively impact the value of a house. These green-amenity upgrades, which other research studies have suggested should be included explicitly in the appraisal process, are a major characteristic of a property when a price estimate is being done. Housing being sold should have a section on the information sheet noting the property green upgrades that exist and an energy efficiency score should be assigned to each house listed for sale.

There are few (if any) academic research papers studying the impact of green tax credits available under the Energy Policy Act (2005) and under the American Recovery and Reinvestment Act (2009). The degree to which green income-tax credits stimulate spending on housing has not been addressed by researchers. This paper is an initial research attempt to quantify whether these legislative efforts measurably encouraged homeowners to adopt newer, greener technologies.

This paper aims to examine potential causes of bankruptcy as they relate to hurricane damage. Investigate whether hurricanes result in personal bankruptcy filings due to real property damages. Strengthen existing descriptive results by using fully modified ordinary least squares (FMOLS).

Lagged FMOLS model is used with data from states that suffered hurricane damage between 2000 through 2020. FMOLS controls for various financial distresses that can cause bankruptcy filings.

Bankruptcy is usually filed for within one year of a hurricane. Changes in house prices and hurricane severity were significant indicators of bankruptcy filings. However, the divorce rate, commonly thought of as a primary reason for bankruptcy, is insignificant.

Data was available on a state level for the independent variables. Hurricane damage needed to be financially significant enough for inland flooding to be measurable and influential.

Establishes that financial distress comes from several sources, not just home damage. Financial distress is highly correlated with whether a home was insured. Divorce does not cause bankruptcy filings.

Federal flood insurance programs should be reexamined. Having a broader all-risk homeowner policy could reduce the number of households that file for bankruptcy after a hurricane.

Existing research uses descriptive statistics and obtains mixed findings regarding the association between hurricane damage and bankruptcy filings. The FMOLS approach provides clarity about this association.

The purpose of this research study is to determine whether flood-damaged residences located in the USA are remaining unrepaired because of the lack of flood insurance coverage. Unrepaired flooded dwellings are subsequently being foreclosed with mortgage-insurance claims being paid to lenders. This paper aims to examine if weather events that cause flooding impact the losses suffered by mortgage insurers and homeowners.

Two fully modified least squares regression models are done using losses experienced by two mortgage insurance companies. The AM Best insurance rating information for a 16-year period or years 2002–2017 is used to study whether the loss ratios experienced by two companies underwriting private mortgage insurance (PMI) are statistically correlated to National Flood Insurance Program (NFIP) claim levels. The assumption is that higher flood insurance claims are a proxy for more severe weather events during a particular year which results in flooding that damage residences.

The NFIP claims coefficient is positive and significant for both companies being examined. This indicates that the more serious the flooding event during a specific year, the higher the losses experienced by the private mortgage insurer. The R² results for the regression models were 0.673–0.695. The income variable has a negative coefficient which was significant. It indicates that falling income lead to rising mortgage insurer losses. The NFIP variable was significant with a positive coefficient.

The mortgage insurance industry is dominated by several companies at any point in time. During the 16-year study period, some companies have become insolvent, merged with other companies or recently started underwriting mortgage insurance. One company was diversified writing multiple lines of property insurance. There were only two insurers with complete financial information for the specified study period.

There are currently five mortgage insurers operating in the USA. A serious flood event could cause the insolvency of some of these companies. This would reduce the competition existing in the default insurance market. The financial markets for real estate loans price mortgages based on the availability and the ability to secure mortgage insurance for high loan-to-value properties. There is federal mortgage insurance available for certain types of residential loans.

There are a limited number of insurers writing flood insurance. These companies can pick or reject dwellings and/or commercial properties to underwrite for insurance. The goal of phasing out insurance through the NFIP may prove impossible to achieve. A flood event without insurance would cause serious financial consequences to property owners, loan delinquencies and could depress the local economy for years. Competition from private mortgage insurers may intensify the adverse selection already being experienced by the NFIP. Private insurers would select the lower risk flood applications leaving the more risky insurance to be covered by the NFIP.

Prior research focused on financial variables impacting PMI and weather factors affecting flood insurance claims. Financial ratios published in the AM Best rating guide for the USA and Canada were used to examine whether or not PMI losses are indirectly affected by flooding events as measured by NFIP variable. Comparing two separate lines of insurance and their impact on each other has not been studied by prior researchers.

San Francisco started regulating short-term vacation rentals on rooms/apartments/houses located within city limits in September 2019. The objectives of this conceptual-scenario and regression study are to calculate the present value of the net earnings for a short-term residential rental property located in San Francisco pre-regulation and post-regulation, and consider a financial reason motivating households to list properties as short-term rentals.

A present value approach is used to estimate the value of rental space to tourists prior to the passage of San Francisco's short-term rental regulations compared to post-rental rules. Table 2 shows pre- and post-income scenarios. Price increases of +20, +40 and +60 percent over the initial base rate failed to restore host earnings to pre-registration levels. The present value model calculates the net revenue less net cost associated with listing a property. The regression model uses the number of listings as the dependent variable, and housing prices divided by weekly wages as independent variables.

The short-term rental regulations significantly reduce the profitability associated with short-term tourist stays offered by hosts and listed by online platforms. A host earns pre-regulation income when average daily rents increase by approximately 71.5 percent. It will likely limit income earned by hosts and Airbnb and other shared housing website platforms due to the reduced number of rental days allowed for shared housing caused by ordinances and host enrollment restrictions. The regression model results suggest that homeowners were listing properties for rent to help cover higher priced property purchases.

Airbnb, VRBO, Booking.com, and HomeAway are all private companies; this means that financial information is not publicly available. HomeAway, VRBO, and Booking.com are companies owned by Expedia. FlipKey is owned by TripAdvisor. Due to limited public information regarding income statements and property listing trends, regression analysis and descriptive statistics cannot be generated using audited financial statements.

Rent control restriction frequently sets the maximum price below the market-clearing price, which results in limited supply but increase in demand for housing. The San Francisco regulations outlaw second-home rentals and seriously limit the availability of other rentals to tourists. FlipKey and HomeAway tend to rent second homes, which San Francisco now bars from being rented for short-term.

The San Francisco restrictions were enacted with the goal of increasing the supply of rental housing available to permanent residents by restricting short-term rentals. This may have limited short-term benefits to permanent residents, but in the long term lowers income associated with single-family housing which will encourage housing arrangements that would avoid leasing restrictions and lower the number of new houses built. Other cities also have a history of rent controls, and are experiencing housing shortages and at the same time attracting large numbers of tourists. These cities may be motivated to enact similar rental restrictions as those approved in San Francisco.

These short-term rental restrictions just started being implemented and enforced. A court decision upheld them. There were media reports outlining the restrictions, but enforcement has just started, so no research papers have been written about San Francisco. Prior research studies have not used net present value analysis to calculate the loss to the host by enacted ordinances restricting tourists’ length of stay and have neither tried to explain why homeowners are listing properties for short-term rentals.

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.

The purpose of this paper is to ascertain whether energy retrofits need to be directed by public policy intervention or can be encouraged through tax relief that harnesses profit incentives. Existing office space potentially has an economic life of 25 to 40 years. It may be operating inefficiently compared to newer buildings for many years. Designing a market-based incentive system that encourages periodic remodeling which lowers energy usage and carbon emissions would have social benefits.

An owner/user case study is developed to test financial feasibility. The empirical study uses publicly available information to examine whether the variables modeled react as anticipated. The regression model incorporates variables of importance to an owner/user. Tax credits and energy deductions, interest rates associated with borrowing and likely electricity and natural gas rate changes are independent variables used to predict the dependent variable new non-residential private construction spending.

Investment tax credits (ITCs) coupled with lending has a positive impact on new non-residential commercial construction spending. The value of these benefits is not sufficient to encourage total building energy retrofits, but would encourage low-cost system upgrades. The interest rates associated with borrowing and the debt-service coverage ratio need to be kept low for existing building energy retrofits to be stimulated.

The case study provides a template that a business can use to determine the financial feasibility of a proposed energy upgrade. It enables the comparison of the marginal cost associated with an update to the present value of the financial benefits likely to be generated. Local real estate tax reductions linked to specific energy upgrades offered by many municipalities can be added to the expected energy savings generated by doing the retrofit.

Tax systems designed to solve environmental pollution problems do not require regulators, inspections or court case decisions and are inherently less intrusive to businesses. Coupling private financial incentives with public policy goals cause energy-saving technologies to be adopted more quickly and with less public outcry.

The paper specifically considers the factors that influence an owner/user of the property. Rental rates and vacancy losses do not influence a property owner/user. Prior studies looked at revenue enhancements and lower-vacancy rates possibly associated with a green compared to a non-green office building. These studies did not focus on the owner/user paradigm. They reported financial benefits accruing to property owners who lease the office building. Many retrofit studies tended to use CoStar Group’s data, which are collected by a for-profit company and sold to users. The data used in this study come from survey data collected by the Federal Government of the United States of America (USA). It is publicly available to all researchers.

Flood damage to uninsured single-family homes shifts the entire burden of costly repairs onto the homeowner. Homeowners in the United States and in much of Europe can purchase flood insurance. The Netherlands and Asian countries generally do not offer flood insurance protection to homeowners. Uninsured households incur the entire cost of repairing/replacing properties damaged due to flooding. Homeowners’ policies do not cover damage caused by flooding. The paper examines the link between personal bankruptcy and the severity of flooding events, property prices and financial condition levels.

A fully modified ordinary least squares (FMOLS) regression model is developed which uses personal bankruptcy filings as its dependent variable during the years 2000 through 2018. This time-series model considers the association between personal bankruptcy court filings and costly, widespread flooding events. Independent variables were selected that potentially act as mitigating factors reducing bankruptcy filings.

The FMOLS regression results found a significant, positive association between flooding events and the total number of personal bankruptcy filings. Higher flooding costs were associated with higher bankruptcy filings. The Home Price Index is inversely related to the bankruptcy dependent variable. The R-squared results indicate that 0.65% of the movement in the dependent variable personal bankruptcy filings is explained by the severity of a flooding event and other independent variables.

The severity of the flooding event is measured using dollar losses incurred by the National Flood Insurance program. A macro-case study was undertaken, but the research results would have been enhanced by examining local areas and demographic factors that may have made bankruptcy filing following a flooding event more or less likely.

The paper considers the impact of the natural disaster flooding on bankruptcy rates filings. The findings may have implications for multi-family properties as well as single-family housing. Purchasing flood insurance generally mitigates the likelihood of severe financial risk to the property owner.

Natural flood insurance is underwritten by the federal government and/or by private insurers. The financial health of private property insurers that underwrite flooding and their ability to meet losses incurred needs to be carefully scrutinized by the insured.

Prior studies analyzing the linkages existing between housing prices, natural disasters and bankruptcy used descriptive data, mostly percentages, when considering this association. The study herein posits the same questions as these prior studies but used regression analysis to analyze the linkages. The methodology enables additional independent variables to be added to the analysis.

The purpose of this paper is to exam the financial impact on the owner/lessor who is considering a partial energy upgrade to an existing medical office building. The owner who leases the building using a triple net lease does the upgrade prior to leasing the building, with the expectation of earning higher rents. How much should the owner who leases the property spend for a given rent per square foot increase?

The empirical study highlights the impact of key financial variables on the dependent variable medical office construction spending put in place in the USA. The independent variables prime interest rate, cost of natural gas per therm and electricity cost per KWH, resale building prices are significant variables when predicting medical office construction spending. A case study using a cost-benefit model is developed. It inputs corporate income tax rates, incorporates a debt service coverage ratio, prime interest rate, analyzes investment tax credit (ITC) and rebate scenarios and varies the level of rental income and energy savings. The case study results provide insight into which factors are enabling higher net construction spending when considering a green energy retrofit project. Both the regression model and the case study model focussed on the owner of a building who rents medical office space to tenants using a triple net lease. The owner/lessor paradigm analyzes revenue enhancements, the tax implications of having these savings and benefits associated with borrowing when financing the green retrofit. The availability of low cost borrowing, increases in the ITC percent and rebates and increases in rent per square foot have an impact on potential energy upgrade spending.

The empirical model finds the independent variables to be significant. Utility cost, resale value of office buildings, the prime interest rate, business bankruptcy court filings and unemployment rate fluctuations adequately explain movements in medical office building spending for the years 2000 through 2015 yielding a R2 of 73.8 percent. The feasibility case study indicates that the energy saving levels and ITCs not income tax rates are the primary drivers for a partial energy retrofit.

Market incentives are a function of the cost of energy. If the cost of energy drops, then the profit incentive to conserve energy becomes less important. The role of tax credits, rebates, property tax reductions and government directives, then become primary incentives for installing energy upgrades. The owner of an empty building assumes all of the operating costs normally paid by a tenant under a triple net lease. This possibility was not included in the replacement cost-benefit model used in this paper.

The feasibility of doing an energy upgrade to an existing building requires that a cost-benefit analysis be undertaken. The independent variables that are significant when doing a regression model or proxies for these variables are incorporated into a present value model. The results in Table V can be used as an initial template for determining how much to spend per square foot when doing an energy upgrade. The square foot amounts can be applied to different size office buildings. The corporate income tax rate or a personal income tax rate has minimal impact on energy construction upgrade spending.

More energy efficient office buildings reduce the amount of greenhouse gases released into the atmosphere. Energy efficient buildings also conserve on scarce fuel reserves. ITCs and rebates limit the role of government in directing decisions to do energy upgrades. The market mechanism to some degree can help encourage energy conservation through asset upgrades.

The paper incorporates an empirical model which is a form of technical analysis to examine independent variables that explain medical office building spending with a case study structured on expected revenues and costs which takes a fundamental approach to understanding the relationship between the dependent variable and its independent variables. The regression model combines factors that impact the demand for energy efficient medical buildings from an owner/lessor perspective which includes resale values of existing buildings, business bankruptcy filings and unemployment rates. Supply independent variables include the prime interest rate and electricity per KWH and natural gas per therm. The regression model found these variables to be significant. The case study uses the same independent variables or close proxy variables to determine the maximum financially feasible per square foot spending that can be invested in energy upgrades.