Foreign investment in Australian residential properties

2019 ◽  
Vol 12 (2) ◽  
pp. 166-180 ◽  
Author(s):  
Hassan F. Gholipour ◽  
Hooi Hooi Lean ◽  
Reza Tajaddini ◽  
Anh Khoi Pham
Keyword(s):  
Real Estate ◽  
Foreign Investment ◽  
House Prices ◽  
Real Estate Market ◽  
Housing Development ◽  
Housing Construction ◽  
Annual Data ◽  
Content Type ◽  
Residential Properties ◽  
The Impact

Purpose The purpose of this study is to examine the impact that foreign investment in existing houses and new housing development has on residential house prices and the growth of the housing construction sector. Design/methodology/approach The analysis is based on a panel cointegration method, estimated using annual data for all Australian states and territories spanning the period of 1990-2013. Findings The results indicate that increases in foreign investment in existing houses do not significantly lead to increases in house prices. On the other hand, a 10 per cent increase in foreign investment for housing development decreases house prices by 1.95 per cent. We also find that foreign real estate investments have a positive impact on housing construction activities in the long run. Originality/value Existing studies used aggregate foreign real estate investment in their analyses. As foreign investment in existing houses and foreign investment for housing development have different impacts on the demand and supply sides of housing market, it is crucial that the analysis of the effects of foreign investment in residential properties on real estate market is conducted for each type differently.

Energy performance certificates in the Turin real estate market

2017 ◽  
Vol 10 (2) ◽  
pp. 149-169 ◽  
Author(s):  
Elena Fregonara ◽  
Diana Rolando ◽  
Patrizia Semeraro
Keyword(s):  
Real Estate ◽  
House Prices ◽  
Energy Performance ◽  
Real Estate Market ◽  
Market Liquidity ◽  
Hedonic Model ◽  
Price Variation ◽  
Content Type ◽  
Time On The Market ◽  
The Impact

Purpose The purpose of this paper is to assess the impact of the Energy Performance Certificate (EPC) on the Italian real estate market, focusing on old buildings. The contribution of EPC labels to house prices and to market liquidity was measured to analyze different aspects of the selling process. Design/methodology/approach A traditional hedonic model was used to explain the variables of listing price, transaction price, time on the market and bargaining outcome. In addition to EPC labels, the building construction period and the main features of apartments were included in the model. A sample of 879 transactions of old properties in Turin in 2011-2014 was considered. Findings A first hedonic model let us suppose that low EPC labels (E, F and G) were priced in the market although EPC labels explained only 6-8 per cent of price variation. A second full hedonic model, which included apartment characteristics, revealed that EPC labels had no impact on prices. Originality/value In Italy EPC has been mandatory for house transactions since 2009, so there are few studies on the effect of EPC on the Italian real estate market at least to our knowledge. Furthermore, unusually for the Italian context, in this paper also transaction prices were analyzed, in addition to the more frequently used listing prices.

scholarly journals Cap rates and risk: a spatial analysis of commercial real estate

2018 ◽  
Vol 35 (1) ◽  
pp. 25-43
Author(s):  
Florian Unbehaun ◽  
Franz Fuerst
Keyword(s):  
Real Estate ◽  
Risk Perceptions ◽  
Large Scale ◽  
Risk Premia ◽  
Real Estate Market ◽  
Commercial Real Estate ◽  
Data Series ◽  
Large Set ◽  
Content Type ◽  
The Impact

Purpose This study aims to assess the impact of location on capitalization rates and risk premia. Design/methodology/approach Using a transaction-based data series for the five largest office markets in Germany from 2005 to 2015, regression analysis is performed to account for a large set of asset-level drivers such as location, age and size and time-varying macro-level drivers. Findings Location is found to be a key determinant of cap rates and risk premia. CBD locations are found to attract lower cap rates and lower risk premia in three of the five largest markets in Germany. Interestingly, this effect is not found in the non-CBD locations of these markets, suggesting that the lower perceived risk associated with these large markets is restricted to a relatively small area within these markets that are reputed to be safe investments. Research limitations/implications The findings imply that investors view properties in peripheral urban locations as imperfect substitutes for CBD properties. Further analysis also shows that these risk premia are not uniformly applied across real estate asset types. The CBD risk effect is particularly pronounced for office and retail assets, apparently considered “prime” investments within the central locations. Originality/value This is one of the first empirical studies of the risk implications of peripheral commercial real estate locations. It is also one of the first large-scale cap rate analyses of the German commercial real estate market. The results demonstrate that risk perceptions of investors have a distinct spatial dimension.

Real estate development in the city of Athens during the financial crisis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maria Nikitidou ◽  
Fragiskos Archontakis ◽  
Athanasios Tagkalakis
Keyword(s):  
Financial Crisis ◽  
Real Estate ◽  
Structural Characteristics ◽  
House Prices ◽  
Real Estate Market ◽  
Structural Factors ◽  
Data Set ◽  
Content Type ◽  
Capital Financing ◽  
The City

Purpose This study aims to determine how the prices of residential properties in the Greek real estate sector are affected by their structural characteristics and by the prevailing economic factors during recession. Design/methodology/approach Based on 13,835 valuation reports for the city of Athens, covering a period of 11 years (2006–2016), this study develops a series of econometric models, taking into account both structural characteristics of the property market and the macroeconomic relevant variables. Finally, the city of Athens is divided into sub-regions and the different effects of the structural factors in each area are investigated via spatial analysis confirming the validity of the baseline model. Findings Findings show that the size, age, level, parking and storage space can explain the property price movements. Moreover, the authors find evidence that it is primarily house demand variables (e.g. the annual average wage, the unemployment rate, the user cost of capital, financing constraints and expectations about the future course of the house market) that affect house prices in a statistically significant manner and with the correct sign. Finally, using a difference-in-differences approach, this study finds that an increase in house demand (on account of net migration) led to higher house prices in smaller and older than in larger and younger apartments in areas with high concentration of immigrants. Originality/value This study uses a novel data set to help entities, individuals and policy-makers to understand how the recent economic and financial crisis has affected the real estate market in Athens.

scholarly journals Modelling the impact of earthquake activity on real estate values: a multi-level approach

2017 ◽  
Vol 10 (1) ◽  
pp. 73-90 ◽  
Author(s):  
Berna Keskin ◽  
Richard Dunning ◽  
Craig Watkins
Keyword(s):  
Real Estate ◽  
Perceived Risk ◽  
House Prices ◽  
Insurance Companies ◽  
Earthquake Risk ◽  
Earthquake Activity ◽  
Content Type ◽  
Multi Level ◽  
Environmental Events ◽  
The Impact

Purpose This paper aims to explore the impact of a recent earthquake activity on house prices and their spatial distribution in the Istanbul housing market. Design/methodology/approach The paper uses a multi-level approach within an event study framework to model changes in the pattern of house prices in Istanbul. The model allows the isolation of the effects of earthquake risk and explores the differential impact in different submarkets in two study periods – one before (2007) and one after (2012) recent earthquake activity in the Van region, which although in Eastern Turkey served to alter the perceptions of risk through the wider geographic region. Findings The analysis shows that there are variations in the size of price discounts in submarkets resulting from the differential influence of a recent earthquake activity on perceived risk of damage. The model results show that the spatial impacts of these changes are not transmitted evenly across the study area. Rather it is clear that submarkets at the cheaper end of the market have proportionately larger negative impacts on real estate values. Research limitations/implications The robustness of the models would be enhanced by the addition of further spatial levels and larger data sets. Practical implications The methods introduced in this study can be used by real estate agents, valuers and insurance companies to help them more accurately assess the likely impacts of changes in the perceived risk of earthquake activity (or other environmental events such as flooding) on the formation of house prices in different market segments. Social implications The application of these methods is intended to inform a fairer approach to setting insurance premiums and a better basis for determining policy interventions and public investment designed to mitigate potential earthquake risk. Originality/value The paper represents an attempt to develop a novel extension of the standard use of hedonic models in event studies to investigate the impact of natural disasters on real estate values. The value of the approach is that it is able to better capture the granularity of the spatial effects of environmental events than the standard approach.

Algorithm of real estate mass appraisal with inequality restricted least squares (IRLS) estimation

2020 ◽  
Vol 13 (2) ◽  
pp. 161-179
Author(s):  
Mariusz Doszyń
Keyword(s):  
Real Estate ◽  
Least Squares ◽  
Econometric Model ◽  
A Priori ◽  
Ordinary Least Squares ◽  
Real Estate Market ◽  
Negative Consequences ◽  
Content Type ◽  
Low Efficiency ◽  
The Impact

Purpose The purpose of this paper is to present an algorithm of real estate mass appraisal in which the impact of attributes (real estate features) is estimated by inequality restricted least squares (IRLS) model. Design/methodology/approach This paper presents the algorithm of real estate mass appraisal, which was also presented in the form of an econometric model. Vital problem related to econometric models of mass appraisal is multicollinearity. In this paper, a priori knowledge about parameters is used by imposing restrictions in the form of inequalities. IRLS model is therefore used to limit negative consequences of multicollinearity. In ordinary least squares (OLS) models, estimator variances might be inflated by multicollinearity, which could lead to wrong signs of estimates. In IRLS models, estimators efficiency is higher (estimator variances are lower), which could result in better appraisals. Findings The final effect of the analysis is a vector of the impact of real estate attributes on their value in the mass appraisal algorithm. After making expert corrections, the algorithm was used to evaluate 318 properties from the test set. Valuation errors were also discussed. Originality/value Restrictions in the form of inequalities were imposed on the parameters of the econometric model, ensuring the non-negativity and monotonicity of real estate attribute impact. In case of real estate, variables are usually correlated. OLS estimators are then inflated and inefficient. Imposing restrictions in form of inequalities could improve results because IRLS estimators are more efficient. In the case of results inconsistent with theoretical assumptions, the real estate mass appraisal algorithm enables having the obtained results adjusted by an expert. This can be important for low quality databases, which is often the case in underdeveloped real estate markets. Another reason for expert correction may be the low efficiency of a given real estate market.

scholarly journals Bridging the gap between real estate research and professional practice in Nigeria

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tunbosun Biodun Oyedokun ◽  
Rotimi Boluwatife Abidoye ◽  
Solomon Pelumi Akinbogun
Keyword(s):  
Real Estate ◽  
Professional Practice ◽  
Academic Research ◽  
Real Estate Market ◽  
Online Questionnaire ◽  
Content Type ◽  
Research Activities ◽  
Research Findings ◽  
Real Estate Research ◽  
The Impact

PurposeBeyond contributing to literature, research findings are expected to reinforce existing best practices while also serving as a springboard for formulating new and more efficient methods of undertaking economic activities. However, academic research is sometimes divorced from implementation and research findings are not always translated into practice. This study, therefore, assesses the impact of real estate research activities and findings on the practice of real estate surveying and valuation in Nigeria as the largest real estate market in Africa.Design/methodology/approachAn online questionnaire survey was conducted to obtain relevant data from Estate Surveyors and Valuers across the country. The survey questions cover reading of academic papers from the field of real estate and the reasons for doing so; whether they have made any changes to their professional practice based on findings from academic papers; and possible barriers to adoption academic research findings in your practice. Mean score ranking and principal component analysis were employed for data analysis.FindingsOut of a total of 61 participants, only 35 have made a change to their professional practice based on findings from academic papers they have read. “Personal development and enlightenment” ranks first on the list of reasons for reading academic papers among the participants while barriers to the adoption of academic research findings relate mainly to education, dissemination and lack of guidance on how to apply research findings.Practical implicationsThe study demonstrates how findings from real estate research are being applied and identifies possible barriers that must be addressed to improve the level of application and consequently, the value of academic studies.Originality/valueThe study provides evidence on barriers to the adoption of academic research and contributes to the global effort to bridge the gap between academia and practice.

Causal effects of immigration inflows on house prices in a traditionally non-destination country

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Woei Chyuan Wong ◽  
Jan-Jan Soon
Keyword(s):  
Fixed Effects ◽  
Positive Impact ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Developed Countries ◽  
Annual Data ◽  
Content Type ◽  
Variable Approach ◽  
The Impact

Purpose The purpose of this study is to examine the causal impact of international immigration inflows on housing prices at the state level in Malaysia from 2007 to 2018. Design/methodology/approach Hedonic regressions using both fixed effects and first difference approaches are used to estimate the impact of immigration inflows on house prices in Malaysia. This study deals with potential endogeneity of immigrants’ choices of destination states in Malaysia by using a shift-share instrument variable approach. Specifically, historical shares of immigrants in a state are used to predict current immigrant inflows to a particular state. The predicted value of immigration flows is then inserted into the house price regression models in place of the actual immigration flows. Findings Using annual data for 14 states from 2007 to 2018, this study documents the positive impact of immigration inflows on house prices in Malaysia. The authors find that a 1% increase in immigration inflows is associated with an increase of 10.2% (first difference) and 13.4% (fixed effects) in house prices. The economic impact is larger in magnitude than that found in developed countries. Contrary to existing studies that find immigration inflows to be associated with native flight, the authors find support for the attraction effects hypothesis, where immigration inflow is positive and significantly related to net native flows. Research limitations/implications The effects of immigration inflows are economically significant, considering that the effects are 10 times larger than those documented in the USA. Policymakers in Malaysia ought to monitor house price trends in immigrant-popular states to ensure that natives are not priced out by new immigrants. Originality/value To the best of the authors’ knowledge, this is perhaps the first study to focus on the relationship between immigration inflows and house prices in Malaysia. Focusing on Malaysia has at least two originality aspects. First, Malaysia is relatively not an immigrant-popular destination. Second, Malaysia has a multiracial and heterogenous society among its natives. The findings, obtained within these two settings, would therefore provide a wider scope of result generalization, and natural experiment grounds for causal implications of our results.

Artificial intelligence algorithms to predict Italian real estate market prices

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Luca Rampini ◽  
Fulvio Re Cecconi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Real Estate ◽  
House Prices ◽  
Housing Prices ◽  
Real Estate Market ◽  
Content Type ◽  
Two Cities ◽  
Artificial Neural

PurposeThe assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.Design/methodology/approachAn extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.FindingsThe models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).Research limitations/implicationsAll the models had an accuracy drop in forecasting the most expensive cases, probably due to a lack of data.Practical implicationsThe accessibility and easiness of the proposed model will allow future users to predict house prices with different datasets. Alternatively, further research may implement a different model using neural networks, knowing that they work better for this kind of task.Originality/valueTo date, this is the first comparison of the three most popular ML models that are usually employed when predicting house prices.

scholarly journals The impact of renewable on-site energy production on property values

2020 ◽  
Vol 13 (3) ◽  
pp. 337-356 ◽  
Author(s):  
Niina Leskinen ◽  
Jussi Vimpari ◽  
Seppo Junnila
Keyword(s):  
Real Estate ◽  
Urban Areas ◽  
Energy Production ◽  
Real Estate Market ◽  
Content Type ◽  
Site Energy ◽  
Influencing Mechanism ◽  
Study Results ◽  
The Impact

Purpose Contrary to the traditional technology project perspective, real estate investors see building-specific renewable energy (on-site energy) investments as part of the property and as something affecting the property’s ability to produce a (net) cash flow. This paper aims to show the value-influencing mechanism of on-site energy production from a professional property investors’ perspective. Design/methodology/approach The value-influencing mechanism is presented with a case study of a prime logistics property located in the Helsinki metropolitan area, Finland. The case study results are compared with the results of a survey answered by over 70 property valuation professionals in the Finnish real estate market. Findings Current valuation practice supports the presented value-creation mechanism based on the capitalisation of the savings generated by a building’s own energy production. Valuation professionals see benefits beyond decreased operating expenses such as enhanced image and better saleability. However, valuers acted more conservatively than expected when transferring these additional benefits to the cash flows of the case property. Practical implications Because the savings in operating expenses can be capitalised into the property value, property investors should consider on-site energy production when the return of on-site energy exceeds the return of the property. This enhances the profitability of on-site energy, especially in urban areas with low initial yields. Originality/value This is the first research paper to open the value-influencing mechanism of on-site energy production from a professional property investors’ perspective in commercial properties and to confirm it from a market study.

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