Selling Prices/Sq. Ft. of Office Buildings in Down town Chicago—How Much Is It Worth to Be an Old But Class A Building?

Dermisi, Sofia; McDonald, John F.

doi:10.1080/10835547.2010.12091270

Cited by 23 publications

(9 citation statements)

References 21 publications

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“…Previous studies examined how the transaction price of office property is influenced by different factors which can be separated into three groups: physical characteristics and related factors; locational factors and other related factors such as the performance of the property itself (Henneberry and Mouzakis, 2014); and regulatory factors (Smith et al, 2009). The influencing factors relating to physical characteristics include the quality and grading of the building (Dermisi and McDonald, 2010;Eichholtz et al, 2010), the area of office building space (Nappi-Choulet et al, 2007), the age of the office building (Dermisi and McDonald, 2010) and the design and energy rating (Fuerst and McAllister, 2011;Bonde and Song, 2013;Eichholtz et al, 2010). In addition to the physical characteristics, previous studies confirmed that the office property values can be affected by accessibility to public transport (Kim et al, 2015;Nitsch, 2006) and also the different types of office submarkets within which the building is located (Dunse et al, 2001).…”

Section: Value Of Office Propertythe Role Of Spatial Dependencymentioning

confidence: 99%

The contribution of spatial dependency to office building price indexes

Liang

Reed

Crabb³

2018

JPIF

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Purpose -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. Design/methodology/approach -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. Findings -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. Research limitations/implications -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. Practical implications -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. Social implications -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. Originality/value -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.

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Section: Value Of Office Propertythe Role Of Spatial Dependencymentioning

confidence: 99%

The contribution of spatial dependency to office building price indexes

Liang

Reed

Crabb³

2018

JPIF

View full text Add to dashboard Cite

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“…Similar conclusions reached by Eichholtz et al (2010) indicate that environmental certification of a commercial building would yield a 2 per cent increase per square foot in rents and add a 16 per cent premium to the selling price. Further research by Dermisi and MacDonald (2010) on building age and office classification suggests a significantly high selling price of Class A property compared to Class B. The premium over Class B properties doubles for historic Class A office buildings constructed before 1972.…”

Section: Jerer 61mentioning

confidence: 99%

Real estate value and quality design in commercial office properties

Nase

Berry

Adair

2013

Journal of European Real Estate Research

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Purpose -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. Design/methodology/approach -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. Findings -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. Practical implications -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. Originality/value -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.

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“…2014), such as floor area (Guttery, 2002;Sirmans et al, 2005;Wang & Chen, 2018) and total number of floors (Colwell et al, 1998;Dermisi & McDonald, 2010), but building area squared has a negative relationship with housing prices (Fisher et al, 2006). Wang and Chen (2018) apply data mining to determine housing prices, and find that the factors affecting housing prices include the area of the house, house age, number of floors, and number of rooms (Aliyev et al, 2019;Keskin, 2008;Chang et al, 2018).…”

Section: Housing Characteristicsmentioning

confidence: 99%

The Effect of the Principal Component Index for Housing Quality Satisfaction on Housing Price: Urban vs. Rural Analysis

Wang¹,

Lee²,

Huang³

2022

IJEF

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The 2015 Questionnaire Survey on Housing Conditions by the Construction and Planning Agency surveyed four categories of satisfactions on housing quality: living convenience, surrounding environmental quality, satisfaction on interior environment, and satisfaction on exterior environment. This study pioneeringly investigates the effect of housing satisfaction on Taiwanese housing price for six municipalities and other rural area. Since the above four survey categories of housing quality are highly overlapping, to avoid variable interaction, we construct an index for housing quality satisfaction using principle component analysis to reduce dimensionality. After controlling the moderation effect of market condition, the results show that residential area, house age, floor location, and number of floors all significantly affect housing price. More importantly, the index for housing quality satisfaction is positively related to housing price, indicating that better housing quality helps in raising housing price. However, the positive relation does not exist in Taipei City or Kaohsiung City. Consistent with previous studies, stress on high housing price may weaken the need for quality consideration, especially the case of Taipei City. Finally, market variation does not lead to difference in the relation between housing quality satisfaction and housing price.

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Selling Prices/Sq. Ft. of Office Buildings in Down town Chicago—How Much Is It Worth to Be an Old But Class A Building?

Cited by 23 publications

References 21 publications

The contribution of spatial dependency to office building price indexes

The contribution of spatial dependency to office building price indexes

Real estate value and quality design in commercial office properties

The Effect of the Principal Component Index for Housing Quality Satisfaction on Housing Price: Urban vs. Rural Analysis

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