Residential property market determinants: evidence from the 2018 Australian market downturn

2019 ◽  
Vol 38 (2) ◽  
pp. 157-175
Author(s):  
Peng Yew Wong ◽  
Woon-Weng Wong ◽  
Kwabena Mintah
Keyword(s):  
Housing Market ◽  
Economic Conditions ◽  
Residential Property ◽  
Investment Fund ◽  
Property Market ◽  
Data Set ◽  
Content Type ◽  
Econometric Methods ◽  
Residential Market ◽  
Australian Bureau Of Statistics

Purpose The purpose of this paper is to validate and uncover the key determinants revolving around the Australian residential market downturn towards the 2020s. Design/methodology/approach Applying well-established time series econometric methods over a decade of data set provided by Australian Bureau of Statistics, Reserve Bank of Australia and Real Capital Analytics, the significant and emerging drivers impacting the Australian residential property market performance are explored. Findings Besides changes in the significant levels of some key traditional market drivers, housing market capital liquidity and cross-border investment fund were found to significantly impact the Australian residential property market between 2017 and 2019. The presence of some major positive economic conditions such as low interest rate, sustainable employment and population growth was perceived inadequate to uplift the Australian residential property market. The Australian housing market has performed negatively during this period mainly due to diminishing capital liquidity, excess housing supplies and retreating foreign investors. Practical implications A better understanding of the leading and emerging determinants of the residential property market will assist the policy makers to make sound decisions and effective policy changes based on the latest development in the Australian housing market. The results also provide a meaningful path for future property investments and investigations that explore country-specific effects through a comparative analysis. Originality/value The housing market determinants examined in this study revolve around the wider economic conditions in Australia that are not new. However, the coalesce analysis on the statistical results and the current housing market trends revealed some distinguishing characteristics and developments towards the 2020s Australian residential property market downturn.

scholarly journals The securitization of residential rental revenue streams in Europe

2017 ◽  
Vol 10 (4) ◽  
pp. 503-518 ◽  
Author(s):  
Jaume Roig Hernando
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Risk Sharing ◽  
Rental Housing ◽  
Cash Flows ◽  
Property Market ◽  
Investment Funds ◽  
Content Type ◽  
The Usa ◽  
Residential Market

Purpose The purpose of this paper is to analyze the securitization of rental streams, a new investment and finance product introduced in the USA in 2013 that enables fundraising from large residential portfolios owned by major investment funds and investment banking. The securities are made up of non-performance loans as well as real estate portfolios of financial entities. Design/methodology/approach An academic analysis of the European securitization market is performed, as well as a broad overview of the state of the art of the rental housing market and investment property market. Moreover, a market study of Real Estate Owned (hereinafter, REOs) and Real Estate Debts is carried out to determine both the present framework and future trends. Various financial entities and real estate management companies are examined through interviews and data collection to assess the reality of distressed assets and residential portfolios owned by major investors. It introduced the Broker’s Price Opinion concept, de loan-to-value concept and the London Interbank Offered Rate. Findings REO-to-rental securitization is a step forward toward the democratization of finance through the globalization of the residential market, improving risk sharing for major and retail investors. The securitization of rental streams in Europe has not taken off, despite several issuances in the USA since 2013 with significant success where first tranches obtained a credit qualification of triple-A from the majority of the main rating agencies. Originality/value At the end of 2013, a global investment firm launched an innovative finance and investment vehicle that securitized the cash flows originating from leased residential properties. That issue resulted in considerable success and in the development of a new alternative and innovative financing source for real estate activity. Taking into account that housing is a primary need of our society, there is a strong motivation for improving the residential market, and thus, REO-to-rental securitization could help take a step forward in making the housing market more efficient.

Analysing the Residential Market in Trójmiasto Using Self-Organizing Map

2021 ◽  
Author(s):  
Olgun Aydin ◽  
Krystian Zielinski
Keyword(s):  
Multidimensional Data ◽  
Residential Property ◽  
Self Organizing Map ◽  
Property Market ◽  
Residential Properties ◽  
Price Trends ◽  
Residential Market ◽  
Key Aspects ◽  
The Impact ◽  
Self Organizing

Although the residential property market has strong connections with various sectors, such as construction, logistics, and investment, it works through different dynamics than do other markets; thus, it can be analysed from various perspectives. Researchers and investors are mostly interested in price trends, the impact of external factors on residential property prices, and price prediction. When analysing price trends, it is beneficial to consider multidimensional data that contain attributes of residential properties, such as number of rooms, number of bathrooms, floor number, total floors, and size, as well as proximity to public transport, shops, and banks. Knowing a neighbourhood's key aspects and properties could help investors, real estate development companies, and people looking to buy or rent properties to investigate similar neighbourhoods that may have unusual price trends. In this study, the self-organizing map method was applied to residential property listings in the Trójmiasto area of Poland, where the residential market has recently been quite active. The study aims to group together neighbourhoods and subregions to find similarities between them in terms of price trends and stock. Moreover, this study presents relationships between attributes of residential properties.

scholarly journals BRE INDEX FOR THE HONG KONG RESIDENTIAL PROPERTY MARKET

2004 ◽  
Vol 8 (2) ◽  
pp. 105-119 ◽  
Author(s):  
Eddie Chi Man Hui ◽  
Joe Tak Yun Wong
Keyword(s):  
Hong Kong ◽  
Housing Price ◽  
Capital Gains ◽  
Residential Property ◽  
Property Market ◽  
Price Expectations ◽  
Significant Difference ◽  
Market Outlook ◽  
Price Trends ◽  
Residential Market

This paper examines housing price trends and prediction, of homeowners and potential home buyers, and establishes an independent index (the BRE Index) based on longitudinal telephone surveys collected. The Index, first of this kind in Hong Kong, measures price expectations and benchmarks the level of housing actors’ confidence in the residential market. This is the first paper delivered as part of a government‐funded research project. It synthesizes the key findings of the first survey mounted from 17th to 20th December, 2003. The results show that confidence among housing actors has begun to grow since the property crash in late 1997 with the “overall” BRE Index standing at 564 (0–1000 range). In general, homeowners, people with higher educational level and higher income are optimistic about the market outlook. Residential property prices are expected to rise marginally in the short term. Statistically, there is no significant difference in housing price expectations between homeowners and non‐owners. In their minds, economic condition is the most important factor affecting housing decisions. Apparently, the rising trends in the immediate past have been used to form expectations. The strength of the association between actual capital gains and forecast capital gains is moderately strong, and there appears co‐movement between them. This leads us to believe that hope‐led expectations increase the likelihood of sustaining price increases. The current market is largely driven by expectations. If households formed their expectations in a similar manner in other periods, there would be similar “positive hit” results, which might render the Index more powerful.

Rent or buy, what are the odds? Analysing the price-to-rent ratio for housing types within the Northern Ireland housing market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Daniel Lo ◽  
Michael James McCord ◽  
John McCord ◽  
Peadar Thomas Davis ◽  
Martin Haran
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Main Property ◽  
Property Market ◽  
Important Indicator ◽  
Content Type ◽  
Rental Market ◽  
Price Series ◽  
The Relationship

Purpose The price-to-rent ratio is often regarded as an important indicator for measuring housing market imbalance and inefficiency. A central question is the extent to which house prices and rents form part of the same market and thus whether they respond similarly to parallel stimulus. If they are close proxies dynamically, then this provides valuable market intelligence, particularly where causal relationships are evident. Therefore, this paper aims to examine the relationship between market and rental pricing to uncover the price switching dynamics of residential real estate property types and whether the deviation between market rents and prices are integrated over both the long- and short-term. Design/methodology/approach This paper uses cointegration, Wald exogeneity tests and Granger causality models to determine the existence, if any, of cointegration and lead-lag relationships between prices and rents within the Belfast property market, as well as the price-to-rent ratios amongst its five main property sub-markets over the time period M4, 2014 to M12 2018. Findings The findings provide some novel insights in relation to the pricing dynamics within Belfast. Housing and rental prices are cointegrated suggesting that they tend to move in tandem in the long run. It is further evident that in the short-run, the price series Granger-causes that of rents inferring that sales price information unidirectionally diffuse to the rental market. Further, the findings on price-to-rent ratios reveal that the detached sector appears to Granger-cause those of other property types except apartments in both the short- and long-term, suggesting possible spill-over of pricing signals from the top-end to the lower strata of the market. Originality/value The importance of understanding the relationship between house prices and rental market performance has gathered momentum. Although the house price-rent ratio is widely used as an indicator of over and undervaluation in the housing market, surprisingly little is known about the theoretical relationship between the price-rent ratio across property types and their respective inter-relationships.

An analysis of operating expense control within US multifamily properties

2017 ◽  
Vol 35 (1) ◽  
pp. 48-66 ◽  
Author(s):  
Andrew Carswell
Keyword(s):  
Explanatory Variable ◽  
Rental Housing ◽  
Positive Association ◽  
Individual Investors ◽  
Property Management ◽  
Property Market ◽  
Data Set ◽  
Content Type ◽  
Operating Income ◽  
The Usa

Purpose The purpose of this paper is to determine the effect that ownership and management structures have on ability to control operating expenses. For individual investors, intensity of management experience is also explored as a possible explanatory variable for operating expenses. For property management services that are contracted out, the level of the fee is investigated as a possible cause for movements in operating expenses as well. Finally, operating expenses are used as a possible explanatory variable for a property’s lease-up performance during the year. Design/methodology/approach The analysis consists of a series of regression models performed on data provided by the 2012 Rental Housing Finance Survey (RHFS) in the USA. The RHFS is a unique data set that covers a wide degree of information on multifamily properties. The RHFS represents 2,260 properties in total, and covers various aspects of the apartment industry, including financing and operational cost measures. Control variables used as independent variables include number of units, year of property acquisition, and age of building. Findings Individual ownership and self-management proved to be statistically significant drivers in driving down log operating expenses. Hours spent by individuals performing property management roles on their own properties had a slightly positive association with operating expenses. For professional managers, the fees devoted solely to the manager or management company had a highly significant and positive effect on other operating costs. Finally, when separating out the individual components of operating expenses, only two variables had significant effects on tenant lease-ups: management expenses (positive) and security expenses (negative). Research limitations/implications The data set is potentially biased toward those properties with less than 100 units, and thus it would be problematic to assume that these findings are generalizable to the population at large. There are also no geographic coding indicators within the RHFS data set, which eliminates the potential to control for various market factors and rural/urban differences. Practical implications The research provides an understanding of some of the basic factors behind increases in operating expenses, which ultimately has implications for performance benchmarks such as net operating income and property market value. Social implications The reasonable controlling of operating expenses ultimately has potentially positive implications for low- to moderate-income populations, who would ultimately experience lower rents as a result. Originality/value This research represents one of the first known uses of the RHFS database.

Boosted regression trees

2014 ◽  
Vol 19 (2) ◽  
pp. 152-167 ◽  
Author(s):  
William J. McCluskey ◽  
Dzurllkanian Zulkarnain Daud ◽  
Norhaya Kamarudin
Keyword(s):  
Real Estate ◽  
Regression Trees ◽  
Heterogeneous Data ◽  
Boosted Regression Trees ◽  
Categorical Variables ◽  
Percentage Error ◽  
Residential Property ◽  
Data Set ◽  
Content Type ◽  
Real Estate Valuation

Purpose – The purpose of this paper is to apply boosted regression trees (BRT) to a heterogeneous data set of residential property drawn from a jurisdiction in Malaysia, with the objective to evaluate its application within the mass appraisal environment in Malaysia. Machine learning (ML) techniques have been applied to real estate mass appraisal with varying degrees of success. Design/methodology/approach – To evaluate the performance of the BRT model two multiple regression analysis (MRA) models have been specified (linear and non-linear). One of the weaknesses of traditional regression is the need to a priori specify the functional form of the model and to ensure that all non-linearities have been accounted for. For a BRT model the algorithm does not require any predetermined model or variable transformations, making the process much simpler. Findings – The results show that the BRT model outperformed the MRA-specified models in terms of the coefficient of dispersion and mean absolute percentage error. While the results are encouraging, BRT models still lack transparency and suffer from the inability to translate variable importance into quantifiable variable effects. Practical implications – This paper presents a useful alternative modelling technique, BRT, for use within the mass appraisal environment in Malaysia. Its advantages include less intensive data cleansing, no requirement to specify the predictive underlying model, ability to utilise categorical variables without the need to transform them and not as data hungry, as for example, MRA. Originality/value – This paper adds to the knowledge in this area by applying a relatively new ML model, BRT to residential property data from a jurisdiction in Malaysia. BRT has shown promise as a strong predictive model when applied in other disciplines; therefore this research empirically tests this finding within real estate valuation.

Transport infrastructure, accessibility and residential property values: evidence from Hong Kong

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jayantha Wadu Mesthrige ◽  
Tayyab Maqsood
Keyword(s):  
Hong Kong ◽  
Fixed Effects ◽  
Property Values ◽  
Transport Infrastructure ◽  
Residential Property ◽  
Fixed Effects Model ◽  
Data Set ◽  
Content Type ◽  
Residential Properties ◽  
Residential Property Values

PurposeHong Kong, like many other developed cities and countries, invests heavily in transport development. This study investigates whether the speculative benefits of future improvements in accessibility, brought about by impending transport development, will be capitalized into nearby residential property values even prior to the opening of the development.Design/methodology/approachDeviating from the standard hedonic price approach, the present study employed a fixed-effects model with a large data set of residential property transactions in the vicinity of three-stations situated along a newly proposed mass-transit-railway line in Hong Kong.FindingsThe results suggest that the values of residential properties close to stations do reflect the accessibility enhancements to be brought about by transport improvements even before the opening of the line. Results revealed a 6.5% of property value premium after the announcement of construction; and higher up to 6.7% after the operation of the line. This indicates that forthcoming new transport-infrastructure development produces changes in spatial price-gradients for neighbouring residential properties. Findings indicate that potential buyers/investors recognized the positive benefits of the planned transportation development, even before completion of the project, and are ready to pay a premium for those properties close to railway stations, representing clear evidence that residential property prices/values, near stations, reflect anticipated accessibility enhancements brought about by transport improvements.Originality/valueThis study, using a novel approach – a fixed-effects model to capture the speculative benefits of future improvements in transport infrastructure – provides a positive hypothesis that expected benefits of future improvements in accessibility are capitalized into property values.

Losses on Dutch residential mortgage insurances

2014 ◽  
Vol 7 (3) ◽  
pp. 307-326 ◽  
Author(s):  
M.K. Francke ◽  
F.P.W. Schilder
Keyword(s):  
Housing Market ◽  
Loss Probability ◽  
Mortgage Default ◽  
Home Equity ◽  
Current Crisis ◽  
Data Set ◽  
Content Type ◽  
Mortgage Insurance ◽  
Dutch Housing ◽  
The Impact

Purpose – This paper aims to study the data on losses on mortgage insurance in the Dutch housing market to find the key drivers of the probability of loss. In 2013, 25 per cent of all Dutch homeowners were “under water”: selling the property will not cover the outstanding mortgage debt. The double-trigger theory predicts that being under water is a necessary but not sufficient condition to predict mortgage default. A loss for the mortgage insurer is the result of a default where the proceedings of sale and the accumulated savings for postponed repayment of the principal associated to the loan are not sufficient to repay the loan. Design/methodology/approach – For this study, the authors use a data set on losses on mortgage insurance at a national aggregate level covering the period from 1976 to 2012. They apply a discrete time hazard model with calendar time- and duration-varying covariates to analyze the relationship between year of issue of the insurance, duration, equity, unfortunate events like unemployment and divorce and affordability measures to identify the main drivers of the probability of loss. Findings – Although the number of losses increases over time, the number of losses relative to the active insurance is still low, despite the fact that the Dutch housing market is the world’s most strongly leveraged housing market. On average, the peak in loss probability lies around a duration of four years. The average loss probability is virtually zero for durations larger than 10 years. Mortgages initiated just prior to the beginning of the financial crisis have an increased loss probability. The most important drivers of the loss probability are home equity, unemployment and divorce. Affordability measures are less important. Research limitations/implications – Mortgage insurance is available for the lower end of the market only and is intended to decrease the impact of risk selection by banks. The analysis is based on aggregate data; no information on individual households, like initial loan-to-value and price-to-income ratios; current home equity; and unfortunate events, like unemployment and divorce, is available. The research uses averages of these variables per calendar year and/or duration. Information on repayments of insured mortgages is missing. Originality/value – This paper is the first to describe the main drivers of losses on insured mortgages in The Netherlands by using loss data covering two housing market crises, one in the early 1980s and the current crisis that started in 2008. Much has changed between the two crises. For instance, prices have risen steeply as has household indebtedness. Furthermore, alternative mortgage products have increased in popularity. Focusing a study on the drivers of mortgage losses exclusively on the current crisis could therefore be biased, given the time-specific circumstances on the housing market.

Modelling energy retrofit investments in the UK housing market

2015 ◽  
Vol 4 (3) ◽  
pp. 251-267 ◽  
Author(s):  
Hassan Adan ◽  
Franz Fuerst
Keyword(s):  
Energy Efficiency ◽  
Housing Market ◽  
Empirical Test ◽  
Investment Decision ◽  
Property Market ◽  
Content Type ◽  
Energy Efficiency Measures ◽  
Energy Retrofit ◽  
Efficiency Measures ◽  
The Uk

Purpose – Improving the energy efficiency of the existing residential building stock has been identified as a key policy aim in many countries. The purpose of this paper is to review the extant literature on investment decisions in domestic energy efficiency and presents a model that is both grounded in microeconomic theory and empirically tractable. Design/methodology/approach – This study develops a modified and extended version of an existing microeconomic model to embed the retrofit investment decision in a residential property market context, taking into account tenants’ willingness to pay and cost-reducing synergies. A simple empirical test of the link between energy efficiency measures and housing market dynamics is then conducted. Findings – The empirical data analysis for England indicates that where house prices are low, energy efficiency measures tend to increase the value of a house more in relative terms compared to higher-priced regions. Second, where housing markets are tight, landlords and sellers will be successful even without investing in energy efficiency measures. Third, where wages and incomes are low, the potential gains from energy savings make up a larger proportion of those incomes compared to more affluent regions. This, in turn, acts as a further incentive for an energy retrofit. Finally, the UK government has been operating a subsidy scheme which allows all households below a certain income threshold to have certain energy efficiency measures carried out for free. In regions, where a larger proportion of households are eligible for these subsidies,the authors also expect a larger uptake. Originality/value – While the financial metrics of retrofit measures are by now well understood, most of the existing studies tend to view these investments in isolation, not as part of a larger bundle of considerations by landlords and owners of how energy retrofits might influence a property’s rent, price and appreciation rate. In this paper, the authors argue that establishing this link is crucial for a better understanding of the retrofit investment decision.

scholarly journals Sentiment-based predictions of housing market turning points with Google trends

2016 ◽  
Vol 9 (1) ◽  
pp. 108-136 ◽  
Author(s):  
Marian Alexander Dietzel
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Turning Points ◽  
House Price ◽  
Data Set ◽  
Content Type ◽  
Forecasting Models ◽  
Search Volume ◽  
Out Of Sample ◽  
Google Search

Purpose – Recent research has found significant relationships between internet search volume and real estate markets. This paper aims to examine whether Google search volume data can serve as a leading sentiment indicator and are able to predict turning points in the US housing market. One of the main objectives is to find a model based on internet search interest that generates reliable real-time forecasts. Design/methodology/approach – Starting from seven individual real-estate-related Google search volume indices, a multivariate probit model is derived by following a selection procedure. The best model is then tested for its in- and out-of-sample forecasting ability. Findings – The results show that the model predicts the direction of monthly price changes correctly, with over 89 per cent in-sample and just above 88 per cent in one to four-month out-of-sample forecasts. The out-of-sample tests demonstrate that although the Google model is not always accurate in terms of timing, the signals are always correct when it comes to foreseeing an upcoming turning point. Thus, as signals are generated up to six months early, it functions as a satisfactory and timely indicator of future house price changes. Practical implications – The results suggest that Google data can serve as an early market indicator and that the application of this data set in binary forecasting models can produce useful predictions of changes in upward and downward movements of US house prices, as measured by the Case–Shiller 20-City House Price Index. This implies that real estate forecasters, economists and policymakers should consider incorporating this free and very current data set into their market forecasts or when performing plausibility checks for future investment decisions. Originality/value – This is the first paper to apply Google search query data as a sentiment indicator in binary forecasting models to predict turning points in the housing market.

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