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This study aims to review the approaches used in the analysis of rental income of residential property in Abuja, Nigeria, to strengthen the existing investment performance approaches initially relied upon by property investors towards having a better and reliable performance evaluation for property investment decision-making.

With the adoption of combined methodological approaches, quantitative data on rental history (2006–2016) were collected on the randomly selected residential investment properties (block of flats) available in the portfolio of estate surveying firms in the different locations/sub-markets of the study area. Data collected were analysed with the frequency mean and growth rate.

All the methodological approaches adopted for analysis displayed varying performance results. No particular sub-market maintains the same ranking position in any of the approaches. The developmental phases previously used as an indication of yield in the study area do not correspond with the status of rental income of sub-markets. Yield has been observed to be a mere attraction to property investment; it does not translate to income growth. Mean income (though a good indicator of changes in rental income) is not a reliable indicator of growth in income, and growth in the rate of income omitted the changes in rental income during the holding period.

The study was restricted to historical rental income data on a block of flat-type residential property, and it does not include capital value analysis or inquire into the factors responsible for variation in rental income during the study period. The outcome of this study is only applicable to a block of 4 number three-bedroom flats residential property type.

Multiple simple methods of analysing rental income performance should be preferred to the single complex method. This will simplify investors’ rental income characteristics of investment towards a better understanding of rental property investment analysis. That rental value appreciates with time does not translate to an increase in the actual rental income of residential investment property.

Through these performance approaches, ranking of the sampled properties in the study area sub-markets will enhance investors’ traditional diversification planning across the study area for an enhanced combination that can achieve latent profitability. The attention of investors is hereby called to these multiple approaches to enable them to merge their investment objectives with any or a combination of these approaches towards making rational investment decisions.

This seems to be the first advocacy for methodological paradigm shift applicable to direct residential property investment performance in Nigeria, using transaction rather than appraisal data.

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.

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.

The mathematics of property valuations commonly used in practice exist in several formulations which have been adopted over the years. All are similar in that they represent simple discounted cash flow models equating the estimated future earnings capacity of a property to a net present (capital) value. The process, whilst appearing somewhat daunting, is in fact accomplished in a manner such that, under normal circumstances, the estimated future cash flow beyond the next rent review is not explicitly expressed. Instead of generating a future income flow (assuming some rate of rental growth) and discounting at a money rate of interest (suitably adjusted for risk), the estimated rental income at the next review is capitalised at a relatively low investment yield rate which merely implies a future rental growth rate.

The quantitative assessment of the degree of risk associated with the direct acquisition of commercial property for investment purposes is practically non‐existent. There is almost always a total reliance on unquantified subjective feeling with no attempt to transform such a qualitative treatment into an analytically more acceptable and useful form. Whilst the investment capitalisation rate should, to an extent, reflect the investor's view of the future earnings capacity of a particular property, this yield rate is principally a function of general market sentiment and may not significantly allow for the inherent risk characteristics of an individual investment. This is especially the case at the prime end of the market where the pressure of funds competing to invest in a sector of particularly limited supply remains most severe.

The recession in the late 1990s in Hong Kong has triggered many commercial building owners to cut operation and maintenance (O&M) cost via outsourcing. The purpose of this paper is to investigate the dominant expenditure on O&M service and examine the relation between extent of outsourcing and rental income of the buildings.

Experienced O&M practitioners were interviewed to collect both quantitative and qualitative data of the commercial buildings they looked after. The in‐house and outsourced O&M costs, air‐conditioning energy costs, rental incomes, and management and air‐conditioning fees for the buildings were analysed.

The total air‐conditioning O&M cost is the principal cost item, with the energy cost being the dominant element. The analysis reveals that the extent of outsourcing bore little correlation with the air‐conditioning O&M cost and the rental income.

Further work may take a similar approach to identify the extent and effect of outsourcing other kinds of services that underpin the core business of an organisation.

Varying the extent of outsourcing O&M service was found to be unpromising for enhancing rental performance or cutting O&M expenditure. This is an important point that the building managers should address when considering whether to go for outsourcing.

The findings imply that in the building O&M service industry, it would be more economical to outsource or undertake in‐house the vast majority of O&M work. The focus for cost minimization should be to improve building energy performance rather than reducing labour resources through outsourcing.

Although rental housing has historically maintained a peripheral position within the community-building sphere, the current economic volatility is evidence of how imbalanced housing policy can impact overall stability, particularly among low-income people within low-income communities. Economic and other macro-environmental shifts will have lasting and poignant impacts on low-income geographies; therefore, the state of rental housing within the context of urban neighborhoods will continue to be a critical policy matter. This research explores whether the low-income housing tax credit (LIHTC) program encourages the development of housing with the physical and operational attributes that strengthen low-income neighborhoods. Given the program's growing dominance, this study analyzes whether specific characteristics associated with neighborhood revitalization are prevalent in LIHTC properties located within qualified census tracts. Also examined are the methodologies among nonprofit developers and for-profit developers relative to these development characteristics.

The findings indicate that properties under 50 units are more likely to be located within suburban qualified census tracts. Within the urban core, the results reveal that qualified census tract LIHTC developments are more often serving extremely low and low-income families. The research outcomes also show that nonprofit developers are more likely to serve lower incomes and utilize certified property management agents for these properties. Given the unique needs of urban and suburban low-income neighborhoods and a national environment that portents a growing dependence upon the LIHTC, the findings suggest that both enhanced coordination between state, regional, and local interests and innovation in resource allocation policy are critical to erasing the neighborhood divide that marginalizes low-income people in low-income communities.

The market value of any property investment will tend to deteriorate over time when compared to similar modern properties if monies are not periodically expended to mitigate the effects of obsolescence. This paper examines the relationship between the initial yield of a property at purchase and the rate of future rental value growth necessary to achieve a criterion rate of return on the investment. Traditionally in calculations of the future rental growth rate required to justify an initial investment yield (when compared, say, to the rental shown by gilt‐edged stocks) the simplistic view is taken that following purchase no further expenditure is anticipated. However, if a property is to maintain its original market appeal (or adapt to evolving circumstances), capital must from time‐to‐time be injected for the purposes of refurbishment. Thus, any analytical model which ignores this inevitable expenditure, but nevertheless assumes a constant rate of long‐term future rental growth, is quite unrealistic. A Refurbishment‐Rental Growth Model is derived which allows the introduction of regular future capital expenditure both in terms of magnitude and frequency. Various examples are illustrated of the effect which such expenditure may have in necessarily increasing the required future rental growth for a property investment in order to achieve an anticipated level of return.

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 article comprises an attempt to find a practical method of applying the decision in the case of the Commissioner for the South African Revenue Service v Woulidge (63 SATC 483) (‘Woulidge’) to limit the application of section 7(3) of the Income Tax Act (‘the Act’). It is proposed in this article that Woulidge would also apply to the provisions of section 7(5) and paragraphs 69 and 70 of the Eighth Schedule to the Act. The approach proposed is illustrated by means of examples. The approach adopted by the Commissioner for the South African Revenue Service is also discussed. A conclusion is drawn regarding the practicality of applying Woulidge in the light of the examples.