Sentiment-based commercial real estate forecasting with Google search volume data

Dietzel, Marian Alexander; Braun, Nicole; Schäfers, Wolfgang

doi:10.1108/jpif-01-2014-0004

Cited by 42 publications

(7 citation statements)

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“…There is no existing measure of property buyers' incentives. We follow the literature (Dietzel et al, 2014;Kulkarni et al, 2009;Wu & Brynjolfsson, 2015) to measure buyers' incentives using Google Trends data. Google Trends does not report the raw level of queries for a given search term.…”

Section: Measuring Prospective Buyers' Incentives In Hong Kongmentioning

confidence: 99%

A robust textual analysis of the dynamics of Hong Kong property market

Wong¹,

Kwong²,

Luk³

et al. 2022

Pacific Economic Review

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Market sentiments influence the dynamics of Hong Kong’s macro‐critical property market, but the unobservable nature of market sentiments makes it difficult to systemically assess this sentiment channel. Using text mining techniques, this paper sets up a news‐based property market sentiment index and a Google Trends‐based buyer incentive index for Hong Kong and studies the sentiment channel of transmission in the Hong Kong property market. The news‐based property market sentiment index can reflect the change in sentiments in past key events, with the sentiments in the primary market tending to lead that of the secondary market during the low housing supply period. For the Google Buyer Incentive Index, we find that it has value‐added in forecasting (or nowcasting) the official property price index. In mapping out the sentiment channel using a structural vector‐autoregressive model, we find that an improvement in market sentiments could stimulate buyers’ incentives, which then together would affect property prices and transaction volumes.

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Section: Measuring Prospective Buyers' Incentives In Hong Kongmentioning

confidence: 99%

A robust textual analysis of the dynamics of Hong Kong property market

Wong¹,

Kwong²,

Luk³

et al. 2022

Pacific Economic Review

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“…Redden (2016) argued that the development of activity nodes such as information technology (IT) Parks, special economic zones (SEZs), and industrial estates boost the city's economy and create a firm employment base in holy cities. Dietzel et al (2014); Glaeser et al (2014) found that there is a huge scope for the service industry in the diasporic cities where the demand for office space is usually of grade A and grade B office spaces. Bamossy (2011) found that the residential sector is a big hit in holy cities attracting both Indian and foreign crowd, who seek to buy their second/retirement homes in the holy city.…”

Section: Extrinsic Attributes and Gentrification Of Holy Citiesmentioning

confidence: 99%

The changing real estate investment dynamics in Indian holy cities: effect of spirituality on property buying behavior

Kaur

Bansal

Solomon

2022

JCHMSD

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PurposeHoly cities in India are seeing tremendous gentrification. This study aims to investigate the effect of the changing lifestyle of people towards spirituality and the changing lifestyle's impact on consumer buying behavior on properties in Indian holy cities which has not been studied anecdotally.Design/methodology/approachThe research is exploratory in nature. A questionnaire has been sent to collect primary data through SurveyMonkey. Simple random sampling was used to collect a sample of 450 respondents which was also verified using G* software. The data were analyzed using descriptive statistics and partial least square–structured equation modeling (PLS-SEM).FindingsFindings obtained through the structural model using bootstrapping technique suggest that intrinsic and extrinsic factors are attracting tourists leading to an increase in the demand for real estate in holy cities.Research limitations/implicationsThe research findings may vary as per the cultural differences and belief in spirituality, which is subject to perceptual biases in different holy cities.Practical implicationsThe traditional determinants of property buying behavior are considered inadequate to attract real estate investments. The inclusion of these behavioral aspects – intrinsic and extrinsic factors may improve the investment inflows in India.Social implicationsSpirituality connects to the concept of behavioral real estate, where the decision to buy property is largely affected by the emotional attachment of people.Originality/valueThis research adds value to fill the gap by finding out the latent determinant – emotional reasons impacting transnational gentrification in India.

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“…The author observed that: (i) the asset prices will be positively affected by stable inflationary policy over the long term, (ii) unemployment figures would continue exhibit downward trend in most major markets in the globe which would lead to a boom in the real-estate sector, (iii) commodity prices that affect consumers and the real estate market would not reflect any significant rise in the near future. Dietzel et al (2014) examined the role of Internet search data in the commercial real estate sector. The authors constructed various forecast models and found that the inclusion of Google search data significantly improves the forecast results of commercial real estate prices for the US market.…”

Section: Related Workmentioning

confidence: 99%

A Time Series Analysis - Based Forecasting Approach for the Indian Realty Sector

Sen¹

2021

Preprint

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<p>Prediction of stock prices using time series analysis is quite a difficult and challenging task since the stock prices usually depict random patterns of movement. However, the last decade has witnessed rapid development and evolution of sophisticated algorithms for complex statistical analysis. These algorithms are capable of processing a large volume of time series data executing on high-performance hardware and parallel computing architecture. Thus computations which were seemingly impossible to perform a few years back are quite amenable to real-time time processing and effective analysis today. Stock market time series data are large in volume, and quite often need real-time processing and analysis. Thus it is quite natural that research community has focused on designing and developing robust predictive models for accurately forecasting stochastic nature of stock price movements. This work presents a time series decomposition-based approach for understanding the past behavior of the realty sector of India, and forecasting its behavior in future. While the forecasting models are built using the time series data of the realty sector for the period January 2010 till December 2015, the prediction is made for the time series index values for the months of the year 2016. A detailed comparative analysis of the methods are presented with respect to their forecasting accuracy and extensive results are provided to demonstrate the effectiveness of the six proposed forecasting models. </p>

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Sentiment-based commercial real estate forecasting with Google search volume data

Cited by 42 publications

References 50 publications

A robust textual analysis of the dynamics of Hong Kong property market

A robust textual analysis of the dynamics of Hong Kong property market

The changing real estate investment dynamics in Indian holy cities: effect of spirituality on property buying behavior

A Time Series Analysis - Based Forecasting Approach for the Indian Realty Sector

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