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Article
Publication date: 5 February 2018

Marcelo Cajias and Sebastian Ertl

The purpose of this paper is to test the asymptotic properties and prediction accuracy of two innovative methods proposed along the hedonic debate: the geographically…

Abstract

Purpose

The purpose of this paper is to test the asymptotic properties and prediction accuracy of two innovative methods proposed along the hedonic debate: the geographically weighted regression (GWR) and the generalized additive model (GAM).

Design/methodology/approach

The authors assess the asymptotic properties of linear, spatial and non-linear hedonic models based on a very large data set in Germany. The employed functional form is based on the OLS, GWR and the GAM, while the estimation methodology was chosen to be iterative in forecasting, the fitted rents for each quarter based on their 1-quarter-prior functional form. The performance accuracy is measured by traditional indicators such as the error variance and the mean squared (percentage) error.

Findings

The results provide evidence for a clear disadvantage of the GWR model in out-of-sample forecasts. There exists a strong out-of-sample discrepancy between the GWR and the GAM models, whereas the simplicity of the OLS approach is not substantially outperformed by the GAM approach.

Practical implications

For policymakers, a more accurate knowledge on market dynamics via hedonic models leads to a more precise market control and to a better understanding of the local factors affecting current and future rents. For institutional researchers, instead, the findings are essential and might be used as a guide when valuing residential portfolios and forecasting cashflows. Even though this study analyses residential real estate, the results should be of interest to all forms of real estate investments.

Originality/value

Sample size is essential when deriving the asymptotic properties of hedonic models. Whit this study covering more than 570,000 observations, this study constitutes – to the authors’ knowledge – one of the largest data sets used for spatial real estate analysis.

Details

Journal of Property Investment & Finance, vol. 36 no. 1
Type: Research Article
ISSN: 1463-578X

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Article
Publication date: 5 February 2018

Marcelo Cajias and Philipp Freudenreich

The purpose of this paper is to examine the market liquidity (time-on-market (TOM)) and its determinants, for rental dwellings in the largest seven German cities, with big data.

Abstract

Purpose

The purpose of this paper is to examine the market liquidity (time-on-market (TOM)) and its determinants, for rental dwellings in the largest seven German cities, with big data.

Design/methodology/approach

The determinants of TOM are estimated with the Cox proportional hazards model. Hedonic characteristics, as well as socioeconomic and spatial variables, are combined with different fixed effects and controls for non-linearity, so as to maximise the explanatory power of the model.

Findings

Higher asking rent and larger living space decrease the liquidity in all seven markets, while the age of a dwelling, the number of rooms and proximity to the city centre accelerate the letting process. For the other hedonic characteristics heterogeneous implications emerge.

Practical implications

The findings are of interest for institutional and private landlords, as well as governmental organisations in charge of housing and urban development.

Originality/value

This is the first paper to deal with the liquidity of rental dwellings in the seven most populated cities of Europe’s second largest rental market, by applying the Cox proportional hazards model with spatial gravity variables. Furthermore, the German rental market is of particular interest, as approximately 60 per cent of all rental dwellings are owned by private landlords and the German market is organised polycentrically.

Details

Journal of Property Investment & Finance, vol. 36 no. 1
Type: Research Article
ISSN: 1463-578X

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Article
Publication date: 13 July 2020

Marcelo Cajias

Digitalisation and AI are the most intensively discussed topics in the real estate industry. The subject aims at increasing the efficiency of existing processes and the…

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488

Abstract

Purpose

Digitalisation and AI are the most intensively discussed topics in the real estate industry. The subject aims at increasing the efficiency of existing processes and the institutional side of the industry is really interested. And in some ways, this is a breakthrough. This article elaborates on the current status quo and future path of the industry.

Design/methodology/approach

The real estate industry is evolving, and parts of the business are increasingly being conquered by “proptechs” and “fintechs”. They have come into real estate to stay not because they discovered inefficiencies in the way one manages and does business with real estate, but because they come with an arsenal of new technologies that can change the whole game. The article discusses a path for changing the game in real estate.

Findings

“location, location, location” has now evolved to “data, data, data”. However, there is one essential aspect that must be considered before the latter can become the real value creator: the ability of market players to analyse data. And this does not mean being an excellent Excel user. The near future sees a solution called Explainable Artificial Intelligence (XAI) meaning that the econometric world constructed decades ago has an expiry date.

Originality/value

One needs to delete two myths from their mind: data quantity is proportional to accurate insights and that bringing your data to a cloud will deliver you with all the insights your business needs almost immediately.

Details

Journal of Property Investment & Finance, vol. 39 no. 1
Type: Research Article
ISSN: 1463-578X

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Article
Publication date: 22 December 2021

Marcelo Cajias and Anett Wins

The paper shows with two concrete examples about how algorithms are used in active real estate management. The paper also highlights that the discussion about the adoption…

Abstract

Purpose

The paper shows with two concrete examples about how algorithms are used in active real estate management. The paper also highlights that the discussion about the adoption of new technologies is crucial for market players.

Design/methodology/approach

The authors review the current status quo about new technologies in real estate and provide two examples of how algorithms can be used to understand locations and the value drivers of rents.

Findings

Location, location, location is nowadays data, data, data coupled with the knowledge of how to create life out of data. Algorithm can help to understand the value drivers of rents and can also help to evaluate the attractiveness of a location.

Practical implications

Real estate management will adapt to new technologies fast. This change has the potential to disrupt exiting strategies due to the increase in efficiency, insights, transparency and location knowledge. Investment managers walking this talk will definitely benefit in future.

Originality/value

The paper makes usage of the latest machine learning technologies applied to real estate investment cases. This is a unique opportunity on bringing light on the discussion about transparency in real estate.

Details

Journal of Property Investment & Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-578X

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Article
Publication date: 24 July 2018

Marcelo Cajias

This paper aims to explore the in-sample explanatory and out-of-sample forecasting accuracy of the generalized additive model for location, scale and shape (GAMLSS) model…

Abstract

Purpose

This paper aims to explore the in-sample explanatory and out-of-sample forecasting accuracy of the generalized additive model for location, scale and shape (GAMLSS) model in contrast to the GAM method in Munich’s residential market.

Design/methodology/approach

The paper explores the in-sample explanatory results via comparison of coefficients and a graphical analysis of non-linear effects. The out-of-sample forecasting accuracy focusses on 50 loops of three models excluding 10 per cent of the observations randomly. Afterwards, it obtains the predicted functional forms and predicts the remaining 10 per cent. The forecasting performance is measured via error variance, root mean squared error, mean absolute error and the mean percentage error.

Findings

The results show that the complexity of asking rents in Munich is more accurately captured by the GAMLSS approach than the GAM as shown by an outperformance in the in-sample explanatory accuracy. The results further show that the theoretical and empirical complexities do pay off in view of the increased out-of-sample forecasting power of the GAMLSS approach.

Research limitations/implications

The computational requirements necessary to estimate GAMLSS models in terms of number of cores and RAM are high and might constitute one of the limiting factors for (institutional) researchers. Moreover, large and detailed knowledge on statistical inference and programming is necessary.

Practical implications

The usage of the GAMLSS approach would lead policymakers to better understand the local factors affecting rents. Institutional researchers, instead, would clearly aim at calibrating the forecasting accuracy of the model to better forecast rents in investment strategies. Finally, future researchers are encouraged to exploit the large potential of the GAMLSS framework and its modelling flexibility.

Originality/value

The GAMLSS approach is widely recognised and used by international institutions such as the World Health Organisation, the International Monetary Fund and the European Commission. This is the first study to the best of the author’s knowledge to assess the properties of the GAMLSS approach in applied real estate research from a statistical asymptotic perspective by using a unique data basis with more than 38,000 observations.

Details

Journal of European Real Estate Research, vol. 11 no. 2
Type: Research Article
ISSN: 1753-9269

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Article
Publication date: 30 April 2019

Marcelo Cajias

This paper aims to develop a conceptual understanding and a methodological approach for calculating residential net initial yields for both a buy-to-hold and rental…

Abstract

Purpose

This paper aims to develop a conceptual understanding and a methodological approach for calculating residential net initial yields for both a buy-to-hold and rental investment strategy from hedonic models.

Design/methodology/approach

The markets modelled comprehend of dwellings for rent and sell in Germany. For each of them, two regression models are estimated to extract implicit prices and rents for an artificial identical dwelling and estimate the willingness to pay for the same asset from both a buy-to-hold and rental investment strategy.

Findings

The 3,381 estimated net initial yields in the 161 German markets showed a spatial pattern with the biggest and most attractive cities showing the lowest yields and a self-adjusting process in the markets surrounding the top cities. The net initial yields over time show that prices have increased stronger than rents, leading to rock bottom yields for residential assets and a significant premium in comparison to government bond yields. The approach responds to the spatial hierarchy of markets in Germany, meaning that the level of the estimated yields is accurate and achievable from an investment perspective.

Practical implications

The investment case in residential markets is certainly unique as net initial yields are scarce, especially due to the relatively low number of investment comparables. The paper sheds light on this problem from a conceptual and methodological perspective and confirms that investment yields are deducible by making usage of hedonic models and big data.

Originality/value

In the era of digitalization and big data, residential assets are mostly brought to the market via digital multiple listing systems. Transparency is an essential barrier when assessing the pricing conditions of markets and deriving investment decisions. Although international brokers do provide detailed investment comparables on – mostly commercial – real estate markets, the residential sector remains a puzzle when it comes to investment yields. The paper sheds light on this problem.

Details

International Journal of Housing Markets and Analysis, vol. 12 no. 4
Type: Research Article
ISSN: 1753-8270

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Article
Publication date: 4 February 2020

Cay Oertel, Jonas Willwersch and Marcelo Cajias

The purpose of this study is to introduce a new perspective on determinants of cross-border investments in commercial real estate, namely, the relative attractiveness of a…

Abstract

Purpose

The purpose of this study is to introduce a new perspective on determinants of cross-border investments in commercial real estate, namely, the relative attractiveness of a target market. So far, the literature has analyzed only absolute measures of investment attractiveness as determinants of cross-border investment flows.

Design/methodology/approach

The empirical study uses a classic ordinary least squares estimation for a European panel data set containing 28 cities in 18 countries, with quarterly observations from Q1/2008 to Q3/2018. After controlling for empirically proven explanatory covariates, the model is extended by the new relative measurement based on relative yields/cap rates and relative risk premia. Additionally, the study applies a generalized additive mixed model (GAMM) to investigate a potentially nonlinear relationship.

Findings

The study finds on average a ceteris paribus, statistically significant lagged influence of the proxy for relative attractiveness. Nonetheless, a differentiation is needed; relative risk premia are statistically significant, whereas relative yields are not. Moreover, the GAMM confirms a nonlinear relationship for relative risk premia and cross-border transaction volumes.

Practical implications

The results are of interest for both academia and market participants as a means of explaining cross-border capital flows. The existing knowledge on determinants is expanded by relative market attractiveness, as well as an awareness of nonlinear relationships. Both insights help to comprehend the underlying transaction dynamics in commercial real estate markets.

Originality/value

Whereas the existing body of literature focuses on absolute attractiveness to explain cross-border transaction activity, this study introduces relative attractiveness as an explanatory variable.

Details

Journal of European Real Estate Research , vol. 13 no. 1
Type: Research Article
ISSN: 1753-9269

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Article
Publication date: 6 February 2017

Marcelo Cajias and Sebastian Ertl

This paper aims to examine whether there are differences between the long and short-term relationship of house prices and interest rates. The elasticity of house prices to…

Abstract

Purpose

This paper aims to examine whether there are differences between the long and short-term relationship of house prices and interest rates. The elasticity of house prices to monetary policy changes, e.g. via interest rates, is from a theoretical perspective and in the long-run negative. However, house prices adapt in the short-run dynamically to economic, financial, institutional and demographic factors.

Design/methodology/approach

In this paper, the authors confirm the aforementioned elasticity for the Nordic housing markets but provide evidence of drastic deviations from the negative relationship. This is done by using rolling regressions in search for time-varying betas.

Findings

The empirical results show that recessionary and expansionary policy regimes play a much more important role in the development of house prices in Finland, Sweden and Norway, than in Denmark.

Originality/value

Further, it is shown that the relationship between house prices and monetary policy is discontinuous over time, with large deviations from the long-term beta during the past decade. This holds true especially since the beginning of the financial crisis and the expansionary monetary policy in Europe.

Details

International Journal of Housing Markets and Analysis, vol. 10 no. 1
Type: Research Article
ISSN: 1753-8270

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Article
Publication date: 29 March 2013

Peter Geiger, Marcelo Cajias and Sven Bienert

Given the growing market awareness concerning responsible investments in recent years, the purpose of this paper is to bridge the gap between real estate companies which…

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1726

Abstract

Purpose

Given the growing market awareness concerning responsible investments in recent years, the purpose of this paper is to bridge the gap between real estate companies which implemented a corporate social responsibility (CSR) agenda and the possible role within a multi‐asset portfolio optimisation framework. The behaviour of the asset class sustainable real estate (SRE) together with its diversification characteristics are the main focus.

Design/methodology/approach

The study is an explorative empirical analysis applying a portfolio optimisation algorithm. First, the authors developed a sustainable real estate index comprehending listed real estate companies from 2004 until 2010 acting in line with a CSR agenda. Second, the authors introduced SRE into the opportunity set of an UK investor and finally, generated the theoretical optimal asset allocation of SRE within different risk‐return portfolios.

Findings

The unique risk‐return pattern of SRE enables the asset class to be allocated across all portfolios ranging from low to high risk along the efficient frontier. In the low‐risk levels, SRE behaves as a diversifier whereas in the medium‐ to high‐risk portfolios SRE is represented as the main allocated asset. Sustainable real estate thus offers opportunities to numerous investors in view of their investment preferences and corporate strategies.

Practical implications

The results could encourage institutional investors to take investments in CSR‐driven listed real estate companies into account and to rethink their strategic asset allocation approach in view of the identified asset characteristics and the behaviour within a portfolio framework.

Originality/value

The paper provides a first insight in the field of portfolio management by introducing SRE into the opportunity set of a UK investor. The study raises SRE to an aggregated level and delivers theoretical as well as empirical evidence of the role sustainable real estate is playing within a multi‐asset portfolio.

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Article
Publication date: 29 March 2013

Marcelo Cajias and Daniel Piazolo

The purpose of this paper is to investigate the effect of energy consumption on the financial performance of German residential buildings in a large panel framework. The…

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2739

Abstract

Purpose

The purpose of this paper is to investigate the effect of energy consumption on the financial performance of German residential buildings in a large panel framework. The authors provide evidence that energy efficiency in the residential sector is a relevant factor affecting both tenant investment decisions and consequently the performance of investor portfolios.

Design/methodology/approach

Based on the IPD Database and information from the German statistical office, the authors create portfolios of buildings across several energy consumption levels in order to describe the energy pricing mechanism in the context of total return and rent price. Furthermore, the authors apply conditional and unconditional regressions over the period of 2008 and 2010, to accurately quantify the energy price premium in the German residential market.

Findings

The descriptive portfolio results show that energy‐efficient buildings yield an up to 3.15 percent higher return and 0.76 €/m2 higher rent than inefficient buildings. Furthermore, the regression results indicate that a one percent decline in energy consumption affects the total return of buildings positively by +0.015 percent. The hedonic results additionally show that one percent energy conservation boosts rent prices by +0.08 percent and market value by +0.45 percent, ceteris paribus.

Originality/value

Overall, the study presents an alternative methodology for describing and estimating hedonic datasets and offers some initial empirical evidence on the energy price premium in German residential markets. The paper contributes to prior European studies regarding the use and implications of energy performance certificates and confirms their significant impact on residential housing performance variables.

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