Modeling Home Prices Using Realtor Data

Pardoe, Iain

doi:10.1080/10691898.2008.11889569

Cited by 17 publications

(12 citation statements)

References 2 publications

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“…The built area (area.build.m2) and the number of bathrooms are traditional attributes that reflect the size of the apartment. Their importance as predictors is consistent with the positive relationship between the attributes of the size and price of the property, as reported in studies such as [27][28][29].…”

Section: Methods With the Best Capacity To Predict Real Estate Pricessupporting

confidence: 84%

Modeling of apartment prices in a Colombian context from a machine learning approach with stable-important attributes

Rave

Echavarría

Correa

2020

DYNA

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The objective of this work is to develop a machine learning model for online pricing of apartments in a Colombian context. This article addresses three aspects: i) it compares the predictive capacity of linear regression, regression trees, random forest and bagging; ii) it studies the effect of a group of text attributes on the predictive capability of the models; and iii) it identifies the more stable-important attributes and interprets them from an inferential perspective to better understand the object of study. The sample consists of 15,177 observations of real estate. The methods of assembly (random forest and bagging) show predictive superiority with respect to others. The attributes derived from the text had a significant relationship with the property price (on a log scale). However, their contribution to the predictive capacity was almost nil, since four different attributes achieved highly accurate predictions and remained stable when the sample change.

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Section: Methods With the Best Capacity To Predict Real Estate Pricessupporting

confidence: 84%

Modeling of apartment prices in a Colombian context from a machine learning approach with stable-important attributes

Rave

Echavarría

Correa

2020

DYNA

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“…The significant results of these interactions indicate that the relationship is nonlinear. Indeed, both POQuantity and DateCheck show nonlinear relationships, and a quadratic formulation (DateCheck^2 and POQuantity^2) fits our data better (Jaccard & Lewis-Back, 2011;Pardoe, 2008). Second, to better understand how our covariates relate to each other, we check our model for multicollinearity.…”

Section: Model Validationmentioning

confidence: 92%

Data‐driven auditing: A predictive modeling approach to fraud detection and classification

Singh

Lai

Vejvar

et al. 2019

J Corp Accounting Finance

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This article develops and empirically tests a predictive model for audit of fraud detection with practical applications for audit operations. By analyzing real‐life accounting data, the proposed model can identify anomalous transactions and directly focus on exceptions for further investigation in real time, thus offering a significant reduction in manual intervention and processing time in audit operations. Our approach is a highly desirable supplement to the existing rule‐based models, given the growing use of information technology for analytics in auditing. The proposed approach is based on classification. Following the tenets of the principal agency theory, we discuss how our approach can help to reduce monitoring and contracting costs, disincentivize fraud, improve auditor efficiency and independence, and increase audit quality. We contribute to the current literature by discussing the implications of data‐driven audit on the moderating role of auditors in principal‐agent relationships and providing practical insights into the operational aspects of financial reporting and auditing, modeling of fraud‐detection classification models, and benefits, barriers, and enablers of implementing data driven audit in companies.

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“…Following the literature, Pardoe (2008), we have decided to add the age 2 transformation variable (age 2 =age Â age) in the analysis of the model. Controlling also for heteroscedasticity, robust standard errors were estimated.…”

Section: Baseline Specifications and Empirical Findingsmentioning

confidence: 99%

Real estate development in the city of Athens during the financial crisis

Nikitidou

Archontakis

Tagkalakis

2021

JERER

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Purpose This study aims to determine how the prices of residential properties in the Greek real estate sector are affected by their structural characteristics and by the prevailing economic factors during recession. Design/methodology/approach Based on 13,835 valuation reports for the city of Athens, covering a period of 11 years (2006–2016), this study develops a series of econometric models, taking into account both structural characteristics of the property market and the macroeconomic relevant variables. Finally, the city of Athens is divided into sub-regions and the different effects of the structural factors in each area are investigated via spatial analysis confirming the validity of the baseline model. Findings Findings show that the size, age, level, parking and storage space can explain the property price movements. Moreover, the authors find evidence that it is primarily house demand variables (e.g. the annual average wage, the unemployment rate, the user cost of capital, financing constraints and expectations about the future course of the house market) that affect house prices in a statistically significant manner and with the correct sign. Finally, using a difference-in-differences approach, this study finds that an increase in house demand (on account of net migration) led to higher house prices in smaller and older than in larger and younger apartments in areas with high concentration of immigrants. Originality/value This study uses a novel data set to help entities, individuals and policy-makers to understand how the recent economic and financial crisis has affected the real estate market in Athens.

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Modeling Home Prices Using Realtor Data

Cited by 17 publications

References 2 publications

Modeling of apartment prices in a Colombian context from a machine learning approach with stable-important attributes

Modeling of apartment prices in a Colombian context from a machine learning approach with stable-important attributes

Data‐driven auditing: A predictive modeling approach to fraud detection and classification

Real estate development in the city of Athens during the financial crisis

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