Real estate price estimation in French cities using geocoding and machine learning

Tchuente, Dieudonné; Nyawa, Serge

doi:10.1007/s10479-021-03932-5

Cited by 39 publications

(21 citation statements)

References 85 publications

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“…As they are commonly used in the literature (see, e.g. Tchuente & Nyawa, 2021, Kumar et al, 2016, 2018, the following measures need to be defined. Accuracy is the proportion of messages correctly predicted by the model among the total number of cases examined.…”

Section: Performance Evaluationmentioning

confidence: 99%

COVID-19 vaccine hesitancy: a social media analysis using deep learning

2022

Self Cite

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Hesitant attitudes have been a significant issue since the development of the first vaccines-the WHO sees them as one of the most critical global health threats. The increasing use of social media to spread questionable information about vaccination strongly impacts the population's decision to get vaccinated. Developing text classification methods that can identify hesitant messages on social media could be useful for health campaigns in their efforts to address negative influences from social media platforms and provide reliable information to support their strategies against hesitant-vaccination sentiments. This study aims to evaluate the performance of different machine learning models and deep learning methods in identifying vaccine-hesitant tweets that are being published during the COVID-19 pandemic. Our concluding remarks are that Long Short-Term Memory and Recurrent Neural Network models have outperformed traditional machine learning models on detecting vaccine-hesitant messages in social media, with an accuracy rate of 86% against 83%.

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Section: Performance Evaluationmentioning

confidence: 99%

COVID-19 vaccine hesitancy: a social media analysis using deep learning

2022

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“…Their findings confirm the superiority of the gradient boosting approach. Other examples can be found at Pace and Hayunga ( 2020 ) and Tchuente and Nyawa ( 2021 ). Based on the concept of gradient boosting, Tianqi and Guestrin ( 2016 ) implement the eXtreme Gradient Boosting (XGBoost) algorithm.…”

Section: Literature Reviewmentioning

confidence: 98%

From human business to machine learning—methods for automating real estate appraisals and their practical implications

Stang

Krämer

Nagl

et al. 2022

Z Immobilienökonomie

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Until recently, in most countries, the use of Automated Valuation Models (AVMs) in the lending process was only allowed for support purposes, and not as the sole value-determining tool. However, this is currently changing, and regulators around the world are actively discussing the approval of AVMs. But the discussion is generally limited to AVMs that are based on already established methods such as an automation of the traditional sales comparison approach or linear regressions. Modern machine learning approaches are almost completely excluded from the debate. Accordingly, this study contributes to the discussion on why AVMs based on machine learning approaches should also be considered. For this purpose, an automation of the sales comparison method by using filters and similarity functions, two hedonic price functions, namely an OLS model and a GAM model, as well as a XGBoost machine learning approach, are applied to a dataset of 1.2 million residential properties across Germany. We find that the machine learning method XGBoost offers the overall best performance regarding the accuracy of estimations. Practical application shows that optimization of the established methods—OLS and GAM—is time-consuming and labor-intensive, and has significant disadvantages when being implemented on a national scale. In addition, our results show that different types of methods perform best in different regions and, thus, regulators should not only focus on one single method, but consider a multitude of them.

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“…Since the proliferation of affordable and granular geospatial data on platform such as OpenStreetMap, Google Maps, Yandex Maps, etc., the use of geographical location features has become increasingly prevalent in automated real estate valuation. Tchuente and Nyawa in their paper evaluate the impact of including granular geographical location features on model performance in the context of the French real estate market [14].…”

Section: Fig 4 Apartment Floor and Building Floors Frequencymentioning

confidence: 99%

Automated Real Estate Valuation With Machine Learning: A Case Study on Apartment Sales in Yerevan

Sergoyan

Bezirganyan

2022

jaer

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Real estate is one of the major sectors of the Armenian economy and has been developing dynamically since Armenia transitions from planned to market economies in early 1990s. More recently, large online platforms have been developed in Armenia to advertise real estate offerings, thus reducing information asymmetry, and increasing liquidity in both sales and rental markets. Simultaneously, granular geospatial data became increasingly affordable via platforms such as OpenStreetMap, Google Maps and Yandex Maps. With granular data concerning a representative portion of the real estate offering available online, it is increasingly tenable to monitor the real estate market in real time and develop analytical tools that can automatically and accurately estimate the value of real estate assets based on their internal and external features. This paper sets out to analyze Armenia real estate market and assess the performance of a special class of machine learning models while predicting the price of a square meter of apartments in Yerevan. Furthermore, it is presented the way to determine the most decisive factors which have an influence on the price of apartments on sale.

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Real estate price estimation in French cities using geocoding and machine learning

Cited by 39 publications

References 85 publications

COVID-19 vaccine hesitancy: a social media analysis using deep learning

COVID-19 vaccine hesitancy: a social media analysis using deep learning

From human business to machine learning—methods for automating real estate appraisals and their practical implications

Automated Real Estate Valuation With Machine Learning: A Case Study on Apartment Sales in Yerevan

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