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The purpose of this paper is to investigate the role of spatial dependency in the construction of a price index for the transactions of whole office buildings. It examines transactions of office buildings over a 15-year period and addresses an under-researched area in investment property analysis.

The study examines data relating to transactions of all office buildings in the Melbourne (Australia) central business district between 2000 and 2015. The methodology uses a spatial weights matrix to construct a hedonic model, spatial error model, spatial lagged model and an office building transactional price index.

The findings confirm the existence of spatial dependency for the transactions of office buildings. In addition, incorporating the effect of spatial dependency by constructing spatial error and spatial lagged model improved the accuracy of the estimated transactional price index for office buildings.

These findings make an important contribution to the literature by highlighting the importance of the issue of spatial autocorrelation in the estimation of valuation models and price indexes for office buildings. Until now the focus has predominantly been on individual office units rather than whole office buildings, where the barrier has traditionally been access to comprehensive data. The analysis did not consider leasing details as this information is not accessible in the Australian market.

The research will assist stakeholders including valuers, investors and market regulators to improve their understanding of movements in the office property transactional market. The findings provide an insight into trends associated with the transfer of office buildings. It will assist future decisions about the location of a new office building developments in order to optimise their proximity to transport and other buildings.

The study will assist planners to ensure the location of office buildings are optimised from a social sustainability perspective. This equates to buildings located in close proximity to transport facilities and also supporting the development of office buildings in locations, which are associated with lower future risk.

The construction of an accurate and reliable property index is critically important for practitioners to understand the movement in both the property market and also in the broader economy. A substantial increase in whole office building acquisitions has been observed in recent years, especially after the 2007 Global Financial Crisis (Lizieri and Pain, 2014) although there has remained limited research undertaken in this area.

While broader property-type categories of real estate markets have been scrutinized at microeconomic level in some segments – namely, residential, retail, industrial and hospitality, there is limited evidence showing that local office markets can be viewed as monolithic and economically integrated entities. The purpose of this paper is to investigate how occupiers differ in their willingness to pay for principal office rent determinants in the corporate and non-corporate sectors.

A sample of properties located in the largest office market in Latin America is partitioned based on the average size of leasable units. This approach captures interactions between different groups of investors and occupiers, and is commonly adopted by local market practitioners due to lack of detailed information on market participants. The pricing schedules for these two groups of buildings are then empirically compared through hedonic regression analysis and parameter stability tests.

The regressions show that corporate and smaller occupier properties form distinct spatial and non-spatial submarkets, but that their temporal patterns are quite similar. Thus, these property-type segments can be classified as imperfect substitutes with distinct pricing schemes, but not as a unique market, as their pricing schedules are not generalizable.

The results imply that “office properties” are too complex and disparate to be reliably examined with a simple aggregate approach as practiced in developed office market research since the 1980s. The fragmented reality of office properties has important implications for investment decisions and real estate valuation.

This paper shows that the corporate office market exhibits distinct characteristics and key determinants of office price and rent valuation differ significantly between the corporate and non-corporate segments. The corollary of these findings is that market studies that require reliable estimates of price drivers may be enriched by modeling these two segmented markets separately. It is also important to note that this distinction cuts across the established A/B/C office space quality classification.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

The purpose of this study is to estimate an empirical model for new office space development starts, based on the theoretical treatment of urban growth. The study introduces a new parameter, namely, office space-usage pattern, to the office space development equation and tests whether developers respond to non-price measures in deciding to commence new developments.

The study first introduces a co-integration approach based on an error correction model to test for long-run relations and short-run dynamics of new office space development. A multivariate regression model is then introduced to identify significant determinants that influence office development starts. The study uses annual data over a time span of 30 years.

Estimated results provide strong evidence that the newly introduced parameter exerts a positive impact on new office space development. It suggests that if the average floor space per employee changes by one percentage point, new office development starts would change by 1.5 percentage point, indicating even a marginal change in floor-space usage per employee (SPE) would have a significant impact on new office space development. Empirical estimates also suggest a strong response of office development starts to the lagged land supply and office space stock.

The paper raises the concern about the importance of non-price measures of the supply-side of the office market. There is scope to address the research questions using better data sets. It is also possible to model the supply adjustment process more dynamically in an error correction framework.

The findings would suggest that non-price measures, such as space-usage pattern, need to be taken into account when planning and estimating future office space needs. This finding provides valuable insight for our current knowledge on factors affecting new office supply.

This is the first study to introduce office floor space usage as a determinant of office development starts in an urban growth conceptual framework for the Hong Kong office market.

This study aims to investigate dynamic relations among office property price indices of 10 major cities in China for the years 2005–2021.

Using monthly data, the authors adopt vector error correction modeling and the directed acyclic graph for the characterization of contemporaneous causality among the 10 indices.

The PC algorithm identifies the causal pattern, and the linear non-Gaussian acyclic model algorithm further determines the causal path from which we perform innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tier of cities.

This suggests that policies on office property prices, in the long run, might need to be planned with particular attention paid to the top tier of cities.

The purpose of this paper is to assess the impact of urban design quality on the real estate value of commercial office property. Empirical evidence based on quantitative research into the added value of quality design on real estate performance has seen little advancement during the past two decades. Office sector hedonic analysis has been predominantly characterised by a piecemeal approach focusing on specific attributes and lacking a holistic approach to the effects of design quality on real estate value. This paper brings forward new empirical evidence to assess the added value of quality design based on quantitative analysis of office sector performance in the historic urban core of a UK city.

Using a unique dataset of 279 Belfast City Centre office properties rented during the period 1995‐2009, this study employs regression analysis to estimate a hedonic pricing model based on a composite range of variables. The contribution of this study is the complementary utilisation of quantitative and qualitative methods to generate variables incorporating a holistic approach to design quality at three different levels of investigation: interior; exterior/architectural; and urban scale.

The key findings show that higher design quality specifications in the three levels (interior, exterior and urban scale) can generate rent premiums. Aspects of quality design that include connectivity and building facade distinctiveness enhance corporate image; and material quality appropriateness adds to real estate value whereas the lack of preference for tall buildings and high‐end interior quality specifications in historic cores reflect market reactions to economic trends.

This paper provides investors and developers with insights about those aspects of quality design that are highly valued by office tenants in historic urban cores. This is especially significant in the downturn of the property cycle with investment priorities playing a crucial role in a project's economic viability.

This study bridges a significant gap in the literature concerning hedonic investigation of the added value of quality design on real estate performance. This holistic approach using quantitative and qualitative methods and incorporating urban design variables constitutes a unique approach to quantifying quality impacts on real estate value.

The purpose of this paper is to estimate the impact of changes in macro-economic conditions going forward, focusing on a change in interest policy, with regard to office letting and investment markets.

For this analysis, the authors constructed two vector-autoregressive models, measuring the response of office rents and capital values in Germany to economic impulses. The authors isolated effects of unique exogenous positive shocks (such as economic growth or interest leaps) on the basis of impulse-response functions in order to understand the complex dynamic interdependence between several economic factors and office performance changes.

The authors initially find a moderately positive development of both office performance components even although supposing an increase in interest level. In terms of capital values, the authors find that they do not drop before 1.5 years after the interest impulse and the negative effect peaks after approximately nine quarters. Furthermore, the reaction to a change in GDP is significantly lower than a reaction to the interest rate, but impulses in other macro-economic factors provoke stronger reactions. Finally, the authors find that a positive interest shock leads to a comparably robust development and economic sustainability in office rents throughout a consideration horizon of 24 quarters.

Estimations are based on observations from a time period containing two rather extraordinary market phases. As they included bubble growth and the low-interest environment, the authors find that certain patterns in both phases neutralize each other when looking at the total time frame. The authors constructed sub-samples to compensate for this. However, the research does not provide to what extent the measured impulse-responses stay forecast-proof, if the market moves into a phase of short-term normalization.

This paper provides insights into estimated impulse-response patterns on a hypothetical sudden increase of several macro-economic determinants. On this basis, the probable reaction to an increase in, for example, the interest rate level can be approximated. Also, the paper provides a fundamental understanding of the economic sustainability of German office properties in terms of their value and rent performance in the case of exogenous shocks.

This paper contains the first vector-autoregressive, impulse-response analysis of office markets in Germany in the context of several macro-economic drivers, including the interest level. It delivers insights into market reaction patterns on the basis of simulated one standard deviation shocks in all included variables.

This paper focuses on a comparison between Internet and traditionally presented and transacted office properties for sale in Boston/US and London/UK. This comparison provides a better understanding of the effect of the Internet on the real estate office market based on a data‐driven, rather than an opinion‐based, study. The study of both cities for a 6 month period, from September 2000 to February 2001, indicated that small properties are more likely to be sold through the Internet than in the traditional office market. Moreover, in both cities, there are differences in the distributions of price per square foot and area in square feet between Internet and traditionally transacted office properties.

As regards the assessment of the market values of properties that compose real estate portfolios, the purpose of this paper is to propose and test an automated valuation model. In particular, the method defined allows for providing for objective, reliable and “quick” valuations of the assets in the phases of periodic reviews of the property values.

Aiming at both predictive and interpretative purposes, the method, based on multi-objective genetic algorithms to search those model expressions that simultaneously maximize the accuracy of the data and the parsimony of the mathematical functions, is applied to a sample data of office properties characterized by medium and large size, located in the city of Milan (Italy) and sold in the period between 2004 and 2015.

The model obtained could be an integration of the canonical methodologies (market approach, income approach, cost approach) implemented in the assessment of the market values of properties, so as to provide an additional tool to verify the results. In particular, the inclusion of economic variables in the model is consistent with the need to reiterate the valuations, contextualizing them to the locational characteristics and to the current property cycle phase in the specific area.

The model can be applied by all the operators involved in the periodic reviews of the values of property portfolios: from real estate funds’ insiders, in order to monitor the values obtained through the canonical approaches, to the public institutions, such as the revenue agencies, in order to ensure the fair payment of the taxes through the updating values of the properties according to the actual and current market trends.

The method proposed can be a valid support for all public and private entities that hold significant property assets and that, for various reasons (periodic reviews of the balance sheets, sales, enhancement, investment, etc.), require cyclical updated values of the properties. The automated valuation model developed can be used for the assessment of “comparison” values with the estimates values obtained by other assessment techniques, in order to ensure a further monitoring tool of the results from the subjects involved.

Technical analysis lies on the premiss that short‐term market price at any time is revealed by pattern of prior price movements. Tests empirically the pattern of the real estate prices by employing the ARIMA analysis. Results strongly show that there exist cyclical trends in the office and industrial property prices in Hong Kong. The forecasting method can provide an indication of short‐term market direction, a sense of whether or not the movement will be small or large, and advance warning well ahead of any turning points supplementary to investment strategy. The investor may wish to incorporate forecasts from an ARIMA model into his investment strategy, for timing purposes.