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This paper presents a hedonic office rent model under the decentralized structure of Istanbul Office Market. The data set in the study includes 2,348 office spaces for the first quarter of 2018. This study aims to find determinants that affect the level of rent and examine whether the effects of office rent determinants are global or not.

To consider both global and local effects, the paper uses mixed geographically weighted regression approach in hedonic office rent analysis.

The empirical results indicate that office rent determinants such as physical, locational, neighborhood and market operational characteristics have significant impacts on the level of the rent. The findings also show that one of the office rent determinants has a global effect and the other determinants have local effects. According to the estimation results, local effects and statistical significances of these determinants vary from lower quartiles to upper quartiles.

To the best of the authors’ knowledge, this is the first paper to consider global and local effects of office rent determinants on the level of rent, with mixed geographically weighted regression approach. The paper provides new insights into the hedonic valuation of commercial real estates, especially for decentralized office markets.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This study aims to analyze whether urban tourism affects Berlin housing rents. Urban tourism is of considerable economic importance for many urban destinations and has developed very strongly over the past few years. The prevailing view is that urban tourism triggers side-effects, which affect the urban housing markets through a lack of supply and increasing rents. Berlin represents Germany’s largest rental market and is particularly affected by growing urban tourism and increasing rents.

The paper considers whether urban tourism hotspots affect Berlin’s housing rents, using two hedonic regression approaches, namely, conventional ordinary least squares (OLS) and generalized additive models (GAM). The regression models incorporate housing characteristics as well as several distance-based measures. The research considers tourist attractions, restaurants, hotels and holiday flats as constituents of tourism hotspots and is based on a spatial analysis using geographic information systems (GIS).

The results can be regarded as a preliminary indication that rents are, indeed, affected by urban tourism. Rents seem to be positively correlated with the touristic attractiveness of a particular location, even if it is very difficult to accurately measure the real quantity of the respective effects of the urban tourism amenities, as the various models show. GAM outperforms the results of OLS and seems to be more appropriate for spatial analysis of rents across a city.

To the best of the authors’ knowledge, the paper provides the first empirical analysis of the effects of urban tourism hotspots on the Berlin housing market.

Multi-let industrial (MLI) estates are an emerging £15 billion UK real estate asset class that can offer attractive returns, a diversified income base, constrained supply and extensive management opportunities to add value within an operational platform. This investment appeal is supported by the evolving MLI occupier market with the growth of small to medium enterprises (SME) requiring modern urban business space driven in part by technology advances offering new streams of supply chain connectivity between businesses and potential clients at a local level.

To understand more about MLI properties, this study utilises a hedonic pricing model to quantify property values as a function of defined variables. The dataset used for this research is a sample portfolio of 26 multi-let industrial properties. The dataset was analysed alongside eleven physical, financial and locational factors. Interestingly, the hedonic pricing model results showed that only four characteristics are value-affecting across the selected properties: namely (1) Granularity of the property income, (2) Distance from the nearest motorway, (3) Distance to the nearest town centre and (4) Gross internal floor area. A chi–test confirmed that there was no significant difference between the modelled values and the supplied property valuations.

This preliminary study offers valuable insight into MLI property market drivers and could easily form a simple decision-making tool to examine potential MLI opportunities in this developing real estate asset class.

In detailing these key MLI property features, current research is limited and focused primarily on market commentary. New knowledge on the MLI property market can provide a platform creating interesting opportunities for fund managers with an intensive management engagement strategy.

This paper examines rent determinants and their relationship with commercial office property rents.

The method adopted in this study differs from that of previous studies on this topic. Firstly, based on the survey of the viewpoints of experts, Relative Importance Index (RII) analysis was used to identify rent determinants and to rank and ensure their relevance and validity in the Sri Lankan context. Secondly, sampling of data related to 115 office properties collected from property tenants and landlords located within the central built-up area of Colombo City was conducted using a multi-methods approach to carry out an objective hedonic analysis of office rents.

This research utilizes RII and hedonic models to provide insights into determinants and relationships. Both analyses confirm that the three top drivers of commercial office rent are distance from the major town center, availability of parking space and the condition of the property. In addition to these three factors, hedonic models reveal that the age of the property and the availability of a conference hall also play a relevant role in explaining office rents. Given the disparities in the findings of the two methods, further examination was able to confirm that factors such as distance from the major town center, parking availability, age of the property, presence of a conference hall, building condition, floor size, business type and type of building are likely to influence commercial office rent. These findings reflect elements such as the quality, newness and better facilities of different office properties.

This systematic study and analysis of office rent for the guidance of real estate investors can support sound investment decisions, potentially leading to more financially sound property development, reduced public debt levels and improved public-private financing. Further, the research findings offer valuable insights to real estate investors, developers and planners regarding location decisions for office development quality enhancements in future office developments.

This research provides fresh insights into the local scale office market, an area where limited evidence currently exists. Further, the methodology adopted provides evidence that hedonic analysis, supported by a multi-method approach, can mitigate the subjective judgments made by professionals.

There are a plethora of putative influencing variables available in the literature for modelling real estate prices using AI. Their choice tends to differ from one researcher to the other, consequently leading to subjectivity in the selection process. Thus, there is a need to seek the viewpoint of practitioners on the applicability and level of significance of these academically established variables.

Using the Delphi technique, this study collated and structured the 35 underlying micro- and macroeconomic parameters derived from literature and eight variables suggested by 11 selected real estate experts. The experts ranked these variables in order of influence using a seven-point Likert scale with a reasonable consensus during the fourth round (Kendall's W = 0.7418).

The study discovered that 16 variables are very influential with seven being extremely influential. These extremely influential variables include flexibility, adaptability of design, accessibility to the building, the size of office spaces, quality of construction, state of repairs, expected capital growth and proximity to volatile areas.

The results of this study improve the quality of data available to valuers towards a fortified price prediction for investors, and thereby, restoring the valuers' credibility and integrity.

The “volatility level of an area”, which was revealed as a distinct factor in the survey is used to add to current knowledge concerning office price. Hence, this study offers real estate practitioners and researchers valuable knowledge on the critical variables that must be considered in AI-based price modelling.

The paper introduced the use of a hybrid system of neural networks support vector machines (NNSVMs) consisting of artificial neural networks (ANNs) and support vector machines (SVMs) to price single-family properties.

The mechanism of the hybrid system is such that its output is given by the SVMs which utilise the results of the ANNs as their input. The results are compared to other property pricing modelling techniques including the standalone ANNs, SVMs, geographically weighted regression (GWR), spatial error model (SEM), spatial lag model (SLM) and the ordinary least squares (OLS). The techniques were applied to a dataset of 3,225 properties sold during the period, January 2012 to May 2014 in Cape Town, South Africa.

The results demonstrate that the hybrid system performed better than ANNs, SVMs and the OLS. However, in comparison to the spatial models (GWR, SEM and SLM) the hybrid system performed abysmally under with SEM favoured as the best pricing technique.

The findings extend the debate in the body of knowledge that the results of the OLS can significantly be improved through the use of spatial models that correct bias estimates and vary prices across the different property locations. Additionally, utilising the result of the hybrid system is thus affected by the black-box nature of the ANNs and SVMs limiting its use to purposes of checks on estimates predicted by the regression-based models.

Air quality, noise and proximity to urban infrastructure can arguably have an important impact on the quality of life. Environmental quality (the price of good health) has become a central tenet for consumer choice in urban locales when deciding on a residential neighbourhood. Unlike the market for most tangible goods, the market for environmental quality does not yield an observable per unit price effect. As no explicit price exists for a unit of environmental quality, this paper aims to use the housing market to derive its implicit price and test whether these constituent elements of health and well-being are indeed capitalised into property prices and thus implicitly priced in the market place.

A considerable number of studies have used hedonic pricing models by incorporating spatial effects to assess the impact of air quality, noise and proximity to noise pollutants on property market pricing. This study presents a spatial analysis of air quality and noise pollution and their association with house prices, using 2,501 sale transactions for the period 2013. To assess the impact of the pollutants, three different spatial modelling approaches are used, namely, ordinary least squares using spatial dummies, a geographically weighted regression (GWR) and a spatial lag model (SLM).

The findings suggest that air quality pollutants have an adverse impact on house prices, which fluctuate across the urban area. The analysis suggests that the noise level does matter, although this varies significantly over the urban setting and varies by source.

Air quality and environmental noise pollution are important concerns for health and well-being. Noise impact seems to depend not only on the noise intensity to which dwellings are exposed but also on the nature of the noise source. This may suggest the presence of other externalities that arouse social aversion. This research presents an original study utilising advanced spatial modelling approaches. The research has value in further understanding the market impact of environmental factors and in providing findings to support local air zone management strategies, noise abatement and management strategies and is of value to the wider urban planning and public health disciplines.