Measuring sentiment in real estate – a comparison study

Heinig, Steffen; Nanda, Anupam

doi:10.1108/jpif-05-2017-0034

Cited by 16 publications

(10 citation statements)

References 47 publications

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“…Relevance strength is a score from 0 (low relevance) to 1 (high relevance) measuring the relevance of the term to the overall text as subject. The significance of including the linguistic analysis of unstructured text data from open-ended survey questions is in the potential information gain for decision makers regarding office design decisions (Heinig and Nanda, 2018). Applying the linguistic analysis tool also offers a repeatable, algorithmic approach that can also be scaled to larger data set sizes impractical or too costly for human interpretation.…”

Section: Methodsmentioning

confidence: 99%

The impact of software company size and culture on commercial real estate location and design

Starr

Worzala

2019

JCRE

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Purpose This paper aims to investigate the relationship of software company culture and core values and project management methodologies on the demand for corporate real estate (CRE), impacting decisions regarding location, square footage, office design and amenities. Design/methodology/approach A researcher-administered survey was designed with the assistance of a purposive sample of brokers, architects and interior designers to elicit responses from the CRE officers in software companies at four stages of growth, from small, entrepreneurial startups to large, publicly traded software companies, located in the same metropolitan area of the USA. Quantitative responses are summarized with traditional statistics and data visualizations. Linguistic analysis, including sentiment analysis and keyword relevance, was performed on the unstructured, English text responses. Findings Differences exist in the office layouts, amenities and locations across the four software company size categories studied. Linguistic analysis of company descriptions of office design, culture and core values, and the relationship between the two, provide another way for brokers, investors and other stakeholders to understand company perspectives and communication idioms related to CRE needs. The research was unable to show any differences in any dependent variable based on software project management methodologies due to sampling limitations. Research limitations/implications This study is limited by the sample size of the participating software companies based on access to company leadership. Results are not generalizable. Practical implications Architects, investors, brokers and lenders may find value in using this study’s approach to better understand the needs of software technology clients. Specifically, stakeholders may find value in examining the linkage from software company size, culture and core values to CRE office layout, amenities and location. Originality/value The qualitative findings suggest that software company culture and core values and company size influence the design of the CRE demanded by software companies. Multivariate data visualization was designed to communicate longitudinal CRE data. Linguistic analysis was used to extract the emotional content and relevance scores from company descriptions of office design, company culture and core values and the reported effect of culture and core values on office design. Findings may be beneficial for stakeholders involved in the design, location and future CRE investments, and they suggest the need for future research on a larger sample.

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Section: Methodsmentioning

confidence: 99%

The impact of software company size and culture on commercial real estate location and design

Starr

Worzala

2019

JCRE

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“…While the role of sentiment in market conditions is easy to understand, the measurement of sentiment in a complex, asymmetrical information economy is not straightforward (Heinig & Nanda, 2018). Relying on direct surveys is a common measure (Case et al, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial Effect of Market Sentiment on Housing Price: Evidence From Social Media Data in China

Wang

Liu

2022

International Journal of Strategic Property Management

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Market sentiment has become more easily spread between cities through social media. This study investigates the spatial effect of market sentiment on housing price in a social media environment. In order to extract home-buyer sentiment from social media, we use text sentiment analysis techniques and build a novel housing market sentiment index. A spatial econometric model of housing price volatility is subsequently constructed and the housing market sentiment index is included as an independent variable in the model. Using panel data from 30 large and medium-sized cities in China for 20 quarters from 2016 to 2020, the spatial effect of market sentiment on housing price is empirically analyzed by calculating direct and indirect effects. The results show that market sentiment had a significant positive effect on housing prices in the local and neighboring cities over the research period. However, the impact of market sentiment on housing price was heterogeneous in terms of geographical region; the direct effect was stronger in the eastern region than in the central and western regions, and the indirect effect was significant only in the eastern region. These findings can provide references for government to formulate housing market regulation policies and measures based on market sentiment.

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“…In two recent studies, Heinig and Nanda (2018) and Heinig et al (2020) present a simple test for the potential of different market indicators to improve a basic cap rate model. Using commercial real estate data for London West End, the results indicate a considerable improvement in model accuracy.…”

Section: Relevant Literaturementioning

confidence: 99%

Asymmetric Patterns of Demand-Supply Mismatch in Real Estate

Marcato

Nanda

2021

J Real Estate Finan Econ

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As expectations change, we may observe asymmetry in responses of economic agents over various phases of the economic cycles. In this paper, we analyze both demand and supply side information to understand the dynamics of price determination in the real estate market and examine the relationship between expectation parameters and demand-supply mismatch. Our hypothesis builds on the possibility that investors’ call for action in terms of their buy/sell decision and adjustment in reservation prices may provide valuable insights into impending demand-supply imbalances in the market. We study several real estate sectors to inform our analysis. The timeframe of our analysis (1995–2010) allows us to observe market dynamics over several economic cycles. We test our hypothesis variously using several measures of market activity within a structural panel VAR framework. Our analysis suggests that investors’ attitude may have substantial and statistically significant feedback effects in price determination. These results indicate noticeable asymmetry in responses during the boom, normal and recessionary periods.

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Measuring sentiment in real estate – a comparison study

Cited by 16 publications

References 47 publications

The impact of software company size and culture on commercial real estate location and design

The impact of software company size and culture on commercial real estate location and design

Spatial Effect of Market Sentiment on Housing Price: Evidence From Social Media Data in China

Asymmetric Patterns of Demand-Supply Mismatch in Real Estate

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