The Value of Design in Real Estate Asset Pricing

Rong, Helena Hang; Yang, Juncheng; Kang, Minkoo; Chegut, Andrea

doi:10.3390/buildings10100178

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…In future, one will endeavour to be more conscious of such themes and explore potential design characteristics at the development scale, such as affordable allocations. Prior to this, Rong et al (2020) conceived of a systematic approach that identifies design attributes for input into financial valuation models. This is the most relatable design study, as researchers identify several fundamental design features for translation into a hedonic pricing model.…”

Section: Literature Reviewmentioning

confidence: 99%

Development risk and unit size within the UK property market

Moore

2022

IJHMA

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Purpose This paper aims to explore the relationship between market pricing and design quality within the development industry. Currently, there is a lack of research that examines real estate at the property level. Development quality is widely believed to have diminished over the past decades, while many investors seem uninterested in the design process. The study aims to address these issues through a pricing model that integrates design attributes. It is hoped that empirical findings will invite broader stakeholder interest in the design process. Design/methodology/approach The research establishes a framework for assessing spatial compliance across residential developments within London. Compliance is assessed across ten boroughs, with technical space guidelines used as a proxy for design quality. Transaction prices and spatial assessments are aligned within a hedonic pricing model. Empirical findings are used to establish whether undermining spatial standards presents a significant development risk. Findings Findings suggest a relationship between sale time and unit size, with “compliant” units typically transacting earlier than “non-compliant” units. Almost half of the 1,600 apartments surveyed appear to undermine technical guidelines. Research limitations/implications It is suggested that an array of design attributes be explored that extend beyond unit size. Additionally, future studies may consider the long-term implications of design quality via secondary transaction prices. Practical implications Practical implications include the development of a more scientific approach to design valuation. This may enhance the position of product design management within the development industry and architectural services. Social implications Social implications may include improvement in residential design. Originality/value An innovative approach combines a thorough understanding of both design and economic principles.

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Section: Literature Reviewmentioning

confidence: 99%

Development risk and unit size within the UK property market

Moore

2022

IJHMA

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“…Considering real estate markets in general, office building markets are more synchronized in terms of exposure to macroeffects and performance of the real estate within the market. The heterogeneity of the office markets makes them more complex to analyse [3], [4]. It can be challenging to understand the market, for which the property's price might be determined on the market, but it may not always equate with the valuation of property in the market [5].…”

Section: Introductionmentioning

confidence: 99%

Rapid Modelling of Machine Learning in Predicting Office Rental Price

Mohd¹,

Harussani²,

Masrom³

2022

IJACSA

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This study demonstrates the utilization of rapid machine learning modelling in an essential case of the real estate industry. Predicting office rental price is highly crucial in the real estate industry but the study of machine learning is still in its infancy. Despite the renowned advantages of machine learning, the difficulties have restricted the inexpert machine learning researchers to embark on this prominent artificial intelligence approach. This paper presents the empirical research results based on three machine learning algorithms namely Random Forest, Decision Tree and Support Vector Machine to be compared between two training approaches; split and crossvalidation. AutoModel machine learning has accelarated the modelling tasks and is useful for inexperienced machine learning researchers for any domain. Based on real cases of office rental in a big city of Kuala Lumpur, Malaysia, the evaluation results indicated that Random Forest with cross-validation was the best promising algorithm with 0.9 R squared value. This research has significance for real estate domain in near future, by applying a more in-depth analysis, particularly on the relevant variables of building pricing as well as on the machine learning algorithms.

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“…For the Building Typology subsystem, in terms of building design [28], 8 typologies were noted, and points were assigned based on the results obtained during interviews with citizens. The points (TS(n)) were assigned as follows:…”

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confidence: 99%

Real Estate Values and Urban Quality: A Multiple Linear Regression Model for Defining an Urban Quality Index

2021

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Urban quality, real estate values and property taxation are different factors that participate in defining how a city is governed. Real estate values are largely determined by the characteristics of urban environments in which properties are located and, thus, by quality of the location. Beginning with these considerations, this paper explores the theme of urban quality through a study of property values that seeks to define all physical (and thus measurable) characteristics that participate in defining urban quality. For this purpose, a multiple linear regression model was developed for reading the residential real estate market in the city of Pescara (Italy). In addition to the intrinsic characteristics of a property (floor area, period of construction/renovation, level, building typology and presence of a garage), input also included extrinsic data represented by the Urban Quality Index. Scientific literature on this theme tells us that many independent variables influence real estate prices, although all are linked to a set of intrinsic characteristics (property-specific) and to a set of extrinsic characteristics (specific to the urban context in which the property is located) and, thus, to the quality of urban environments. The index developed was produced by the analytical and simultaneous reading of four macrosystems with the greatest impact on urban quality: environment, infrastructure, settlement and services (each with its own subsystems). The results obtained made it possible to redefine proportional ratios between various parts of the city of Pescara, based on a specific Urban Quality Index, and to recalculate market property values used to calculate taxes in an attempt to resolve the inequality that persists in this field.

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The Value of Design in Real Estate Asset Pricing

Cited by 6 publications

References 32 publications

Development risk and unit size within the UK property market

Development risk and unit size within the UK property market

Rapid Modelling of Machine Learning in Predicting Office Rental Price

Real Estate Values and Urban Quality: A Multiple Linear Regression Model for Defining an Urban Quality Index

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