The impact of location on housing prices: applying the Artificial Neural Network Model as an analytical tool.

The location of a residential property in a city directly affects its market price. Each location represents different values in variables such as accessibility, neighbourhood, traffic, socio-economic level and proximity to green areas, among others. In addition, that location has an influence on the choice and on the offer price of each residential property. The development of artificial intelligence, allows us to use alternative tools to the traditional methods of econometric modelling. This has led us to conduct a study of the residential property market in the city of Valencia (Spain). In this study, we will attempt to explain the aspects that determine the demand for housing and the behaviour of prices in the urban space. We used an artificial neutral network as a price forecasting tool, since this system shows a considerable improvement in the accuracy of ratings over traditional models. With the help of this system, we attempted to quantify the impact on residential property prices of issues such as accessibility, level of service standards of public utilities, quality of urban planning, environmental surroundings and other locational aspects.

Advanced property valuation techniques and valuation accuracy: Deciphering the artificial neural network technique

Property valuation end-users generally rely on property value opinion provided by valuers in making informed real estate investment decisions. However, the inaccuracy of valuation estimates could be attributed to the adoption of inappropriate property valuation methods and such inaccurate estimates could mislead real estate investors and stakeholders. This could result in individual loss and national loss due to the importance of the real estate sector to the national economy. Therefore, this study aims to examine the application of advanced property valuation techniques with special emphasis on the artificial neural network (ANN) technique in estimating accurate property values. A detailed review of the literature on issues involved in property valuation was conducted. The issues presented in this paper include the origin of ANN, its strength and weaknesses in comparison with other valuation approaches, its application both in theory and in practice, requirements for application in property valuation, valuers' response to its adoption, amongst others. It was found that the ANN technique could produce on the average accurate and reliable estimates but has not been widely adopted in practice. Thereafter, the challenges ahead in bridging the gap between theory and practice of the application of artificial intelligence (AI) techniques were discussed. In addition, the strategies of facilitating this paradigm shift to achieve a global sustainable property valuation practice are presented in this paper

Automated Valuation Models (AVMs): Machine Learning, namely Mass (Advanced) Valuation Methods and Algorithms

Digitalisation is becoming increasingly common within the valuation sector. Thus, it is vital to understand how traditional valuation methods are being replaced by machine learning technology, namely mass (advanced) valuation methods. According to Soni and Sadiq (2015: 100), real estate markets are popular with investors, who are keen to identify a fast way to play the market or to hedge against existing volatile portfolios. Therefore, an accurate prediction of house price is essential to prospective home owners, developers, investors, valuers, tax assessors, mortgage lenders and insurers. Demirci, O (2021) stated that the fluctuation and the relationship between value, worth, and risk remain unchanged in the current market. This means that the increased use of Automated Valuation Models (AVMs) requires a discussion of the machine learning technology, namely mass (advanced) valuation methods, which are the fundamental basis of the algorithms used within the valuation sector. As defined by Erdem (2017), valuation can be categorised into traditional, statistical and modern methods. This Research Paper will investigate both the statistical and modern methods of valuation and their application to the real estate valuation. In particular, it will look at the main limitations of the traditional valuation methods in respect to their accuracy, consistency and speed (Jahanshiri, 2011; Wang & Wolverton, 2012; Adetiloye & Eke, 2014). Moreover, these methods will be compared against mass (advanced) valuation methods, when there is a need to value a group of properties. Indeed, with the increasing volume of transactions and changing marketplace of real estate, mass (advanced) valuation has been widely adopted in many countries for different purposes, including assessment of property tax (Osborn, 2014). https://doi.org/10.13140/RG.2.2.12649.4208

Does artificial intelligence enhance house price forecasting accuracy? – a literature review

The fast-changing housing industry demands the adoption of advanced approaches to valuation for a quick, reliable and accurate result. The traditional approach for forecasting house prices, called the Hedonic Pricing Model (HPM), is problematic given its inaccuracy due to problems with heteroskedasticity and multicollinearity among variables in the model. Recently, there is increasing attention in the application of Artificial Intelligence (AI) to forecast house prices. AI, through Artificial Neural Network (ANN), addresses the shortcomings of HPM. Hence, this paper aims to critically review previous studies on the ability of ANN as a substituted model for HPM in forecasting house prices. Various secondary sources were involved due to extracting various documentary data. It was concluded that the application of AI-enhanced forecasting is accurate. This was demonstrated through the superior predictive performance of ANN compared to HPM

PROPERTY VALUE ASSESSMENT USING ARTIFICIAL NEURAL NETWORKS, HEDONIC REGRESSION AND NEAREST NEIGHBORS REGRESSION METHODS

In this paper, hedonic regression, nearest neighbors regression and artificial neural networks methods are applied to the real and up to date estate data set belongs to Adana province of Turkey. Traditionally, hedonic regression methods have been used to predict house prices. Because of the nature of the relationships between the factors affecting house prices are generally being nonlinear; some alternative methods have been needed. Nearest neighbors regression (Knn) and artificial neural networks (Ann) present both flexible and nonlinear fittings. Classical hedonic approach and its nonlinear alternatives have been employed on a mixed types data set and compared based on some performance measures including root mean squared error, r squared, the coefficient of determination, and mean absolute error. Cross validation method has been used to determine the appropriate model parameters for nearest neighbors and Ann. According to the results, Ann is found better when compared to other methods in terms of all measures. Besides, Knn regression method provides reasonable results despite of lower performance than hedonic regression method. It has been seen that Ann is a powerful tool for predicting house prices

O Uso de Redes Neurais na Engenharia de Avaliações: Determinação dos Valores Venais de Imóveis Urbanos

O presente trabalho tem por objetivo a utilização da técnica de Redes Neurais Artificiais na estimação dos valores venais de imóveis urbanos da cidade de Guarapuava, PR. Para tanto, utilizaram-se dados do Cadastro Imobiliário fornecidos pelo setor de Planejamento da Prefeitura Municipal. O modelo inicial foi composto por treze variáveis/atributos do cadastro: bairro, setor, pavimentação, esgoto, iluminação pública, área do terreno, pedologia, topografia, situação, área edificada, tipo, estrutura e conservação. A técnica da Análise das Componentes Principais foi usada para reduzir e transformar as variáveis originais em nove fatores. As Redes Neurais Artificiais desenvolvidas foram do tipo feedforward, utilizando o algoritmo de treinamento Levenberg-Marquardt, com uma camada oculta. Os resultados da amostra de dados completa foram comparados com os resultados obtidos dividindo-se a mesma em grupos menores, compostos de bairros com características semelhantes, sendo que os resultados obtidos com estes últimos (grupos menores) foram superiores aos obtidos com o primeiro (amostra completa)

South American Expert Roundtable : increasing adaptive governance capacity for coping with unintended side effects of digital transformation

This paper presents the main messages of a South American expert roundtable (ERT) on the unintended side effects (unseens) of digital transformation. The input of the ERT comprised 39 propositions from 20 experts representing 11 different perspectives. The two-day ERT discussed the main drivers and challenges as well as vulnerabilities or unseens and provided suggestions for: (i) the mechanisms underlying major unseens; (ii) understanding possible ways in which rebound effects of digital transformation may become the subject of overarching research in three main categories of impact: development factors, society, and individuals; and (iii) a set of potential action domains for transdisciplinary follow-up processes, including a case study in Brazil. A content analysis of the propositions and related mechanisms provided insights in the genesis of unseens by identifying 15 interrelated causal mechanisms related to critical issues/concerns. Additionally, a cluster analysis (CLA) was applied to structure the challenges and critical developments in South America. The discussion elaborated the genesis, dynamics, and impacts of (groups of) unseens such as the digital divide (that affects most countries that are not included in the development of digital business, management, production, etc. tools) or the challenge of restructuring small- and medium-sized enterprises (whose service is digitally substituted by digital devices). We identify specific issues and effects (for most South American countries) such as lack of governmental structure, challenging geographical structures (e.g., inclusion in high-performance transmission power), or the digital readiness of (wide parts) of society. One scientific contribution of the paper is related to the presented methodology that provides insights into the phenomena, the causal chains underlying “wanted/positive” and “unwanted/negative” effects, and the processes and mechanisms of societal changes caused by digitalization

An Optimal House Price Prediction Algorithm: XGBoost

An accurate prediction of house prices is a fundamental requirement for various sectors, including real estate and mortgage lending. It is widely recognized that a property’s value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighborhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning (ML) techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare XGBoost, support vector regressor, random forest regressor, multilayer perceptron, and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction. Our findings present valuable insights and tools for stakeholders, facilitating more accurate property price estimates and, in turn, enabling more informed decision making to meet the housing needs of diverse populations while considering budget constraints

The next wave of disruption: Emerging market media use of artificial intelligence and machine learning

In frontier and emerging media markets across the globe, there are many new opportunities in newsrooms to innovate through artificial intelligence, machine learning and data processing. In this report, IMS, The Fix and the Latin American Centre for Investigative Journalism (The CLIP) have drawn the lens to fast-rising developmental changes capable of driving digital transformation in business and journalism by understanding how those newsrooms can use technology to develop a data and user-led approach to newsgathering, content, distribution, marketing and sales, and post-sale services

From the Digital Data Revolution toward a Digital Society : Pervasiveness of Artificial Intelligence

Technological progress has led to powerful computers and communication technologies that penetrate nowadays all areas of science, industry and our private lives. As a consequence, all these areas are generating digital traces of data amounting to big data resources. This opens unprecedented opportunities but also challenges toward the analysis, management, interpretation and responsible usage of such data. In this paper, we discuss these developments and the fields that have been particularly effected by the digital revolution. Our discussion is AI-centered showing domain-specific prospects but also intricacies for the method development in artificial intelligence. For instance, we discuss recent breakthroughs in deep learning algorithms and artificial intelligence as well as advances in text mining and natural language processing, e.g., word-embedding methods that enable the processing of large amounts of text data from diverse sources such as governmental reports, blog entries in social media or clinical health records of patients. Furthermore, we discuss the necessity of further improving general artificial intelligence approaches and for utilizing advanced learning paradigms. This leads to arguments for the establishment of statistical artificial intelligence. Finally, we provide an outlook on important aspects of future challenges that are of crucial importance for the development of all fields, including ethical AI and the influence of bias on AI systems. As potential end-point of this development, we define digital society as the asymptotic limiting state of digital economy that emerges from fully connected information and communication technologies enabling the pervasiveness of AI. Overall, our discussion provides a perspective on the elaborate relatedness of digital data and AI systems.publishedVersionPeer reviewe