Can digital technologies speed up real estate transactions?

Saull, Andrew; Baum, Andrew; Braesemann, Fabian

doi:10.1108/jpif-09-2019-0131

Cited by 46 publications

(36 citation statements)

References 23 publications

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“…Especially in the course of transactions, it is all the more important to carry out a structured data transfer. The data transfer from the previous to a new owner is frequently subject to an inefficient and delayed process (Saull and Baum, 2019). Through digital processing, human capacities can be focused on value-adding processes and efficiency can be increased.…”

Section: Resultsmentioning

confidence: 99%

Fundamentals for automating due diligence processes in property transactions

Müller

Päuser²,

Kurzrock

2020

JPIF

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PurposeThis research provides fundamentals for generating (partially) automated standardized due diligence reports. Based on original digital building documents from (institutional) investors, the potential for automated information extraction through machine learning algorithms is demonstrated. Preferred sources for key information of technical due diligence reports are presented. The paper concludes with challenges towards an automated information extraction in due diligence processes.Design/methodology/approachThe comprehensive building documentation including n = 8,339 digital documents of 14 properties and 21 technical due diligence reports serve as a basis for identifying key information. To structure documents for due diligence, 410 document classes are derived and documents principally checked for machine readability. General rules are developed for prioritized document classes according to relevance and machine readability of documents.FindingsThe analysis reveals that a substantial part of all relevant digital building documents is poorly suited for automated information extraction. The availability and content of documents vary greatly from owner to owner and between document classes. The prioritization of document classes according to machine readability reveals potentials for using artificial intelligence in due diligence processes.Practical implicationsThe paper includes recommendations for improving the machine readability of documents and indicates the potential for (partially) automating due diligence processes. Therefore, document classes are derived, reviewed and prioritized. Transaction risks can be countered by an automated check for completeness of relevant documents.Originality/valueThis paper is the first published (empirical) research to specifically assess the automated digital processing of due diligence reports. The findings are helpful for improving due diligence processes and, more generally, promoting the use of machine learning in the property sector.

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Section: Resultsmentioning

confidence: 99%

Fundamentals for automating due diligence processes in property transactions

Müller

Päuser²,

Kurzrock

2020

JPIF

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“…to significantly accelerate all transactions related to the sale and rental of real estate, their valuation, etc. [3]. The value of buildings is also affected by their location in the landscape as such.…”

Section: Literary Researchmentioning

confidence: 99%

“…The function is used for the calculation, which determines the percentage of costs of site location depending on the size of the enclosed space of the building. For individual categories of the enclosed space of the building in the interval <0 m 3 ; 100,000 m 3 >, the percentage rate of costs of site location is determined using the linear function:…”

Section: Costs Of Site Locationmentioning

confidence: 99%

Methodology for Calculating the Reproduction Value of Buildings

et al. 2021

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The article deals with the description of the methodology for calculating the reproduction value of buildings. It is a mathematical model that uses data obtained from a statistical survey and calculates the total reproduction value of buildings from a large data set. The model works with many variable inputs that describe the valued building. These include, for example, the use of the building, the enclosed space of the building, the floor area of the building, the building material of the supporting structures of the building, the age of the building, the technical condition of the building, the year the building was acquired and others. Based on the results, the model was calibrated and its parameters optimized to achieve the most accurate values.

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“…In an environment where efficient real estate deals and reduction in transaction costs are important, there are calls for fast-tracking the process by using standardised contracts; one such solution is the model commercial lease (MCL) (Williams, 2019) while another is the just released RICS Code for Leasing Business Premises, which outlines best practices that surround key lease elements such as rent and rent reviews [1]. Furthermore, Saull et al (2020) conducted interviews and focus groups with real estate professionals and found that the combination of standardisation, the sharing of data and the adoption of digital technologies, including AVMs, have the potential to increase market transparency, improve liquidity and lower transaction costs. Lastly, Somani (2019) advocates lease standardisation, as it promotes automation that increases transaction speeds and fosters a more transparent property market.…”

Section: Introductionmentioning

confidence: 99%

Pricing office rents in Sydney CBD: testing the water on automated rent reviews

Filippova

Gabe

Rehm

2021

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PurposeAutomated valuation models (AVMs) are statistical asset pricing models omnipresent in residential real estate markets, where they inform property tax assessment, mortgage underwriting and marketing. Use of these asset pricing models outside of residential real estate is rare. The purpose of the paper is to explore key characteristics of commercial office lease contracts and test an application in estimating office market rental prices using an AVM.Design/methodology/approachThe authors apply a semi-log ordinary least squares hedonic regression approach to estimate either contract rent or the total costs of occupancy (TOC) (“grossed up” rent). Furthermore, the authors adopt a training/test split in the observed leasing data to evaluate the accuracy of using these pricing models for prediction. In the study, 80% of the samples are randomly selected to train the AVM and 20% was held back to test accuracy out of sample. A naive prediction model is used to establish accuracy prediction benchmarks for the AVM using the out-of-sample test data. To evaluate the performance of the AVM, the authors use a Monte Carlo simulation to run the selection process 100 times and calculate the test dataset's mean error (ME), mean absolute error (MAE), mean absolute percentage error (MAPE), median absolute percentage error (MdAPE), coefficient of dispersion (COD) and the training model's r-squared statistic (R2) for each run.FindingsUsing a sample of office lease transactions in Sydney CBD (Central Business District), Australia, the authors demonstrate accuracy statistics that are comparable to those used in residential valuation and outperform a naive model.Originality/valueAVMs in an office leasing context have significant implications for practice. First, an AVM can act as an impartial arbiter in market rent review disputes. Second, the technology may enable frequent market rent reviews as a lease negotiation strategy that allows tenants and property owners to share market risk by limiting concerns over high costs and adversarial litigation that can emerge in a market rent review dispute.

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Can digital technologies speed up real estate transactions?

Cited by 46 publications

References 23 publications

Fundamentals for automating due diligence processes in property transactions

Fundamentals for automating due diligence processes in property transactions

Methodology for Calculating the Reproduction Value of Buildings

Pricing office rents in Sydney CBD: testing the water on automated rent reviews

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