Rental Growth Research Articles

Finance and the supply of housing quality

I show how financial intermediaries affect rental housing quality and affordability by supplying real estate investors with financing for quality improvement projects (i.e., renovations). First, I document a historic surge in improvement activity since the Great Recession. Then, using exogenous variation generated by a 2015 change in regulatory capital requirements, I find that a reallocation of bank credit toward improvement projects accounts for 24% of quality improvements since 2015. The shock increases the supply of high-quality apartments and lowers their rent. However, it raises the average apartment’s rent and accounts for 32% of historically high rent growth over 2015–16.

The Globalization of Local Housing Markets: Immigrants, the Motherland and Housing Prices in Australia

The impact of immigrants on housing prices and rents has been well documented in the literature. There has been less research, however, on other mechanisms by which global economic and financial developments may impact on local housing markets. We investigate whether the foreign-born—that is the stock of previous immigrants—act as a conduit for economic changes abroad to influence local housing markets. Examining disaggregated regions in Australia from 2006-16, we construct a measure of the average performance of the motherland economies of the foreign-born for each region. We find evidence that house prices and rental growth tend to rise when motherland economies are performing poorly. This effect is economically meaningful, robust and appears to represent a distinct channel from the immigration effect.

Slum growth in Brazilian cities

This paper studies slum growth in Brazilian cities by estimating a spatial equilibrium model with two types of households and two types of housing. I explain the positive correlation observed between slum growth and economic growth in cities with three IV-estimated structural elasticities. The number of low-income households in a city increases with wages with an elasticity of 1.7. Housing rents increase with housing demand with elasticities of 0.37 for non-slum housing and 0.07 for slum housing. Economic growth in cities thus causes slum growth because it attracts large number of low-income households who face higher rent growth for non-slum than slum housing. I further show that as economic growth improves households’ incomes nationwide, an income effect reduces the national slum share. Finally, I find that two policy-relevant counterfactuals significantly increase aggregate income by relocating households into high-wage cities: 1) slum upgrading in high-wage cities 2) reducing the elasticity of non-slum housing rents to housing demand shocks.

The 2000s Housing Cycle with 2020 Hindsight: A Neo-Kindlebergerian View

With “2020 hindsight,” the 2000s housing cycle is not a boom-bust but rather a boom- bust-rebound at both the national level and across cities. We argue this pattern reflects a larger role for fundamentally-rooted explanations than previously thought. We construct a city-level long-run fundamental using a spatial equilibrium regression framework in which house prices are determined by local income, amenities, and supply. The fundamental predicts not only 1997-2019 price and rent growth but also the amplitude of the boom-bust-rebound and foreclosures. This evidence motivates our neo-Kindlebergerian model, in which an improvement in fundamentals triggers a boom-bust-rebound. Agents learn about the fundamentals by observing “dividends” but become over-optimistic due to diagnostic expectations. A bust ensues when over-optimistic beliefs start to correct, exacerbated by a price-foreclosure spiral that drives prices below their long-run level. The rebound follows as prices converge to a path commensurate with higher fundamental growth. The estimated model explains the boom-bust-rebound with a single fundamental shock and accounts quantitatively for cross-city patterns in the dynamics of prices and foreclosures.

Flattening the Curve: Pandemic-Induced Revaluation of Urban Real Estate

We show that the COVID-19 pandemic brought house price and rent declines in city centers, and price and rent increases away from the center, thereby flattening the bid-rent curve in most U.S. metropolitan areas. Across MSAs, the flattening of the bid-rent curve is larger when working from home is more prevalent, housing markets are more regulated, and supply is less elastic. Housing markets predict an urban revival with urban rent growth exceeding suburban rent growth for the foreseeable future, as working from home recedes.

The 2000s Housing Cycle With 2020 Hindsight: A Neo-Kindlebergerian View

With 2020 hindsight,'' the 2000s housing cycle is not a boom-bust but rather a boom-bust-rebound at both the national level and across cities. We argue this pattern reflects a larger role for fundamentally-rooted explanations than previously thought. We construct a city-level long-run fundamental using a spatial equilibrium regression framework in which house prices are determined by local income, amenities, and supply. The fundamental predicts not only 1997-2019 price and rent growth but also the amplitude of the boom-bust-rebound and foreclosures. This evidence motivates our neo-Kindlebergerian model, in which an improvement in fundamentals triggers a boom-bust-rebound. Agents learn about the fundamentals by observing dividends'' but become over-optimistic due to diagnostic expectations. A bust ensues when over-optimistic beliefs start to correct, exacerbated by a price-foreclosure spiral that drives prices below their long-run level. The rebound follows as prices converge to a path commensurate with higher fundamental growth. The estimated model explains the boom-bust-rebound with a single fundamental shock and accounts quantitatively for cross-city patterns in the dynamics of prices and foreclosures.

Alquiler vacacional en destinos turísticos consolidados: la venganza de los actores que ya no importan

The objective of this research is the vacation rental growth in tourist destinations; Tenerife Is‑ land is taking like a case study. Specifically, we analyzed the influence of the accommodation structure on the expansion process of this business model; and, on the other hand, the impact that the vacation rental may have on the rules of the game in the tourist field. It starts from a theoretical perspective that analyzes economic spaces as complex productive realities, made up of actors and capitals, which interact on the basis of behavioral guidelines based on norms and (un) equilibria of power. Methodologically, we proceed by means of a coherent, systematic and triangular approach from different data sources. As for the results, there is an increase in vacation rental, partially conditioned by the existing tourism model, and also an open and conflicting debate regarding the operating rules in the sector

Location Density, Systematic Risk, and Cap Rates: Evidence from REITs

A property's location is often considered to be the ultimate determinant of its investment performance. But how exactly does a property's location influence its risk and return? We focus on the effects of location density on the risk and return of commercial real estate investments. We do this by studying the geographical characteristics of the property portfolios of U.S. equity REITs. We show that REITs with property holdings in high-density locations experience higher rental growth and carry higher systematic risk than their otherwise comparable peers in low-density locations. Consistent with higher rental growth rates, high-density REITs also have lower implied cap rates. Our results suggest that location density is an important determinant of REIT performance outcomes, implying that geographical characteristics can drive investment risk and return across commercial real estate markets.

Spreadsheet Iteration of Reversionary Leasehold Rental Growth Rate Within The Framework of Explicit DCF Appraisals

Income growth rates are required to justify decisions and strategies for property investments. Although existing studies addressed this phenomenon in freehold investments, a relative question regarding the determination of rental growth rates of leasehold investment properties valued part-way through rent review periods has not been addressed before now. This study examined the spreadsheet-assisted scenario analysis tools and techniques that are required for the determination of rental growth rates of leasehold investment properties valued part-way through rent review periods. A precursor to the scenario analysis was the development of a hybrid leasehold DCF valuation model arising from the equation of the formula for reversionary leasehold equivalent yield valuation to the formula for reversionary leasehold growth explicit DCF valuation model; thereby culminating into the identification of four unknown variables comprising the all risks yield and the implied growth rates of leasehold cash inflows and cash outflows which were subsequently derived using the solver tool of Excel®. From a total of eleven scenarios generated, the 9th successive scenario produced optimal results indicating zero slack between iterated and calculated values for the growth rates of leasehold cash inflows and cash outflows respectively. With recourse to the hybrid leasehold DCF valuation model, the spreadsheet-assisted scenario was found to produce mathematically valid growth rates that justify the valuation of leasehold investment properties part-way through rent review periods. The value of this research is the analytical tools and rigour it avails investors seeking income returns and growth from reversionary leasehold property as an instance of terminable investments.

Residual-augmented IVX predictive regression

Bias correction in predictive regressions is known to reduce the empirical size problems of OLS-based predictability tests with persistent predictors. This paper shows that bias correction is also achieved in the context of the extended instrumental variable (IVX) predictability testing framework introduced by Kostakis et al. (2015). To be specific, new IVX-based statistics subject to a bias correction analogous to that proposed by Amihud and Hurvich (2004) are introduced. Four important contributions are provided: first, we characterize the effects that bias-reduction adjustments have on the asymptotic distributions of the IVX test statistics in a general context allowing for short-run dynamics and heterogeneity; second, we discuss the validity of the procedure when predictors are stationary as well as near-integrated; third, we conduct an exhaustive Monte Carlo analysis to investigate the small in- and out-of-sample properties of the test procedures and their sensitivity to distinctive features that characterize predictive regressions in practice, such as strong persistence, endogeneity, and non-Gaussian innovations; and fourth, we provide an analysis of real estate return and rent growth predictability in 19 OECD countries.

House Prices and Rents

Monthly cross-section regressions show that price-to-rent ratios and lagged growth rates of house prices for eleven U.S. metro areas predict variation in the cross-section of rent growth rates. Details of the interplay between house prices and rents pose open questions. What generates the 1998-2012 boom and bust in house prices relative to rents and the subsequent 2012-2020 surge in prices and rents? Why are similar wide swings in house prices relative to rents absent for at least 45 years prior to 1998? General patterns in house prices and rents are shared by many metropolitan areas, but there are large differences of degree, and some areas pass entirely on the boom and bust in 1998-2012 house prices. What explains this diversity?

Bubbling Away: Forecasting Real Estate Prices, Rents, and Bubbles in a Transition Economy

Real housing prices have both surged and swooned in formerly socialist countries. We use 2000–2017 aggregate housing sales and rental price data from Kazakhstan to explore price movements during boom and stagnation eras, investigating the rent–price ratio’s (RPR) capacity to predict housing returns and rent growth for an emerging post-Soviet economy. RPR predicts returns better during periods of price increases than declines, and its importance in predicting the bubble component diminishes with time. Short-run RPR changes are consistent with rational bubble behavior during the period of secular upswing but different predictive variables matter during price increases and declines.

Stickiness of rental rate and housing vacancy rate

We study the stickiness in house rent by examining rent under two settings: rent is determined solely by landlord and rent is set by Nash bargaining between landlord and tenant. Our results show that, under Nash bargaining, the vacancy rate is able to restrain the growth of rents and hence generates the stickiness feature in rents.

Factors Influencing Rental and Capital Values of Residential Investment Property in Abuja, Nigeria

There are various purposes the rental or capital values of residential investment property could be sought. However, most previous studies on factors influencing value of residential investment property are expressed in a general term; not specifically indicating whether rental or capital value. This may mislead investors in making the wrong investment decision. Using aggregation and disaggregation approach, this study examined the general and specific factors determining the rental and capital values of residential investment property in Abuja, Nigeria with a view to providing information that could guide the investment decisions. With the use of a structured questionnaire, cross sectional survey was adopted to obtain the perceptual opinion from 136 estate surveying and valuation firms in the study area. Data was analyzed with descriptive statistics of Relative Important Index (RII). The findings reveal a variation in the outcome of general factors and specific factors and that in specific term, factors influencing the capital value are significantly more in quantity and rating than the ones influencing the rental value. Out of the 23 (13 for rental and 10 for capital value) general determining factors, 11 (3 for rental and 8 for capital value) are actually significant. Average number of rooms; change in maintenance cost/taxes and the neighbourhood characteristics mainly influence the rental value of residential property while cost of construction; type of structure/quality of facilities; potential of rental growth; title and size of the land; neighbourhood attributes; inflation impact; size and structural characteristics and state of supply in property market are the main factors influencing the capital value. This noted variation is an indication for property investment stakeholders to be cautious and specific in the selection of the most appropriate determining factors for their investment objective to avoid investment decision errors.

A Model of the Australian Housing Market

We build an empirical model of the Australian housing market that quantifies interrelationships between construction, vacancies, rents and prices. We find that low interest rates (partly reflecting lower world long‐term rates) explain much of the rapid growth in housing prices and construction over the past few years. Another demand factor, high immigration, also helps explain the tight housing market and rapid growth in rents in the late 2000s. A large part of the effect of interest rates on dwelling investment, and hence GDP, works through housing prices.

Resurgent landlordism in a student city: urban dynamics of private rental growth

ABSTRACT Many countries have seen a remarkable revival of private-rental housing markets in recent years. Academic literature so far has focused on theorizing the political-economic drivers of reinvestment in the tenure or on charting aggregate trends. This paper adds to these literatures in several ways based on a fine-grained analysis of housing market transformations in Groningen, a medium-sized university city in The Netherlands. First, we reveal the variegated trajectories through which private-rental growth materializes on the ground and untangle the role of different types of landlords. While small-scale private landlords remain dominant, we find a clear and important trend toward property concentration. Second, we highlight variations in spatial investment strategies across landlord types. Third, we reveal how contemporary dynamics of increased landlordism play out in a medium-sized city, embedded in a context of national private rental resurgence and local housing market pressures of a growing student city.

The unlikely revival of private renting in Amsterdam: Re-regulating a regulated housing market

Over the last decade, private rental sectors have been in rapid ascendance across developed societies, especially in economically liberal, English-speaking contexts. The Netherlands, and Amsterdam in particular, has also more recently experienced the reversal of a century-long decline in private renting. More unusually, the expansion of private renting in Amsterdam has been explicitly promoted by the municipal and national government, and in cooperation with social housing providers, in response to decreasing accessibility to, and affordability of, social rental and owner-occupied housing. This paper explores how and why this state-initiated revival has come about, highlighting how new growth in rent-liberalized private renting is a partial outcome of the restructuring of the urban housing market around owner occupation since the 1990s. More critically, our analysis asserts that restructuring of Amsterdam’s housing stock can be conceptualized as regulated marketization. Market forces are not being simply unleashed, but given more leeway in some regards and matched by new regulations. We also demonstrate various tensions present in this process of regulated marketization; between national and local politics, between existing housing and new construction, and between policies implemented in different time periods.

Determinants of land value volatility in the U.S. Corn Belt

ABSTRACT Understanding land value volatility and its reaction to exogenous shocks helps land owners, investors, and lenders assess risk. Land value volatility, the variance of the unpredictable component of land value growth rates, is modelled for each of the Corn Belt states in the U.S. using EGARCH. A pooled VAR system is then estimated to capture the interactions between land value determinants and land value volatility. The variables of the pooled VAR are split into negative and positive vectors to allow for asymmetric impacts. Impulse response functions are mapped. All states exhibit land value volatility clustering. Inflation, cash rent and population growth rates granger cause land value volatility. Land value volatility responses to negative shocks are greater than those to positive shocks. Lenders and investors should expect greater swings in land values after negative shocks to land value growth rates, but not an overreaction of land values from shocks to cash rent growth rates. Positive shocks to changes in interest rates increases land value volatility, but unexpected shocks to population growth rates do not have statistically significant impact on land value volatility.

What Moves the German Land Market? A Decomposition of the Land Rent-Price Ratio

The price increases on agricultural land markets over the last decade have triggered a debate about land as an attractive investment opportunity for agricultural and non-agricultural investors. In a static environment, the rent-price ratio provides a first indicator of the profitability of an investment in land. In this paper, we apply the dynamic Gordon growth model to Western Germany and decompose the rent-price ratio into the expected present values of rental growth rates, real interest rates, and a land premium, i.e., the excess return on investment. This analysis reveals that the recent price surge on agricultural land markets was not unprecedented; that the land market rent-price ratio is rather low and varies considerably among federal states; and that (expected) premia for land are mostly negative. Finally, we find that changing expected present values of returns on land investments are the major driver for land price volatility.

Commercial Real Estate and Migration: What Can the Employment Composition of Local Job Markets Tell Us About Future Demand?

Demographics and population growth are among the most important factors affecting real estate markets. Differences in population growth between U.S. metros are driven largely by domestic migration patterns. What explains migration patterns, and can this help anticipate future conditions in commercial real estate markets? We examine the structure of local job markets and find that metros with higher concentrations of certain professions like management, business and finance, have persistently higher in-migration, while metros with greater focus on production, transport (and “material moving”) saw lower in-migration or net outmigration. Jobs in computer, science, and engineering were the largest driver for international migration. These migration patterns have a statistically significant impact on apartment and office markets, including higher rent growth, larger price increases, stronger demand growth and new construction. Among metros like Seattle and Austin, high levels of construction may be warranted by future in-migration in response to the composition of professions in the local job market.