The Study on Fractal Characteristics of Television Audience Ratings Based on R/S Analysis Method

According to the nonlinear distribution of Television Audience ratings data, a fractal dimension study on average daily television audience ratings of three TV stations based on R/S analysis method. Results show that the Hurst index of time series is significantly greater than 0.5 and there is a trend of long-term memory; All the time series is significantly different in the non-periodic cycles length and offer explanation from the perspective of three Television stations’ characteristics of development. This method can help television managers realize the viewing situation, master the change rule and help advertisers make a scientific decision

Property Rental Value Classification Model: A Case of Osogbo, Osun State, Nigeria

Residential property rental value forecasting has an impact on property investment decision. This necessitates the need for a study to forecast residential property rental value considering all associated variables including presence of cultural sites in the study area. Data for the study were gathered from the record of recent lettings in the study area. For the purpose of precision, this study adopted three artificial intelligence models. These are artificial neural network, logistic regression and support vector machine as models of classifying the rental value of residential property in Osogbo. The study considered relevant input variables among which are distance to cultural site, age of building, state of exterior/interior of building. Findings from the study revealed that the three adopted forecasting models had over 80% of the forecasted properties correctly classified thus making the residential property rental forecasting very reliable. Also, it was established that, in the study area, distance from cultural site is the property attribute with the highest negative impact on rental value

Determinants of Residential Property Value in Nigeria – A Neural Network Approach

This study investigated, by means of artificial intelligent system, theinfluence of residential real estate property characteristics on propertyvalues (prices) in Nigeria, using two major cities (Benin and Lagos) asexamples. It revealed a high positive linear correlation between propertycharacteristics and the property market values; an indication that thesecharacteristics reasonably predict property market values. The studydemonstrated that although several property characteristics can beidentified with residential real estate properties, only are few importantones have significant impact on the market values of such properties. Itidentified nine (9) property characteristics that have relatively strongimpact on market values (prices) and to that extent influence the salesand purchase decisions of sellers and buyers in Nigeria. The results ofthe study should enable Real Estate Professionals to make fair estimatesof the market values of residential real estate properties given the features/characteristics of such housing units. This would aid rapidvaluation, help to improve housing quality and make possible massevaluation of properties. The study recommends to real estatepractitioners and other professionals, amongst others, to use theknowledge of significant property features/characteristics for moreefficient valuation, improved quality of their sales/purchase decisionsand proper management of residential housing units

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

Using Genetic Algorithms for Real Estate Appraisal

The main aim of this paper is the interpretation of the existing relationship between real estate rental prices and geographical location of housing units in a central urban area of Naples (Santa Lucia and Riviera of Chiaia neighborhoods). Genetic algorithms (GA) are used for this purpose. Also, to verify the reliability of genetic algorithms for real estate appraisals and, at the same time, to show the forecasting potentialities of these techniques in the analysis of housing markets, a multiple regression analysis (MRA) was applied comparing results of GA and MRA

Explorando el uso de la inteligencia artificial en la maximización de precios para el sector turístico: su aplicación en el caso de Airbnb en la Comunidad Valenciana

The use of machine learning is becoming more and more frequent in companies’ search for competitiveness. Literature on the subject show us how in many cases artificial intelligence can help companies to improve their knowledge about users, optimize prices or guide buyers in their choices. To confirm that the application of artificial intelligence models allows companies to obtain specifically better price optimisation procedures than with other traditional models, we have studied more than 10,000 Airbnb properties in the three main cities in the Valencian Community (Valencia, Alicante and Castellón), noting that the estimation process using neural networks offers significantly more satisfactory results than the use of hedonic models.El empleo del aprendizaje automático es cada vez más frecuente para explicar la competitividad de las empresas. La literatura nos muestra cómo la inteligencia artificial puede ayudar a empresas a mejorar su conocimiento de los usuarios, optimizar los precios o guiar a los compradores en su proceso de elección. Para confirmar que aplicando modelos de inteligencia artificial se permite obtener específicamente mejores procedimientos de optimización de precios respecto a otros modelos tradicionales, se estudian más 10.000 propiedades de Airbnb en las tres capitales de la Comunidad Valenciana (Valencia, Alicante y Castellón), observando que los resultados obtenidos con el modelo de redes neuronales artificiales son significativamente más satisfactorios que con el empleo de modelos hedónicos

Forecasting property price indices in Hong Kong based on grey models

The real estate market in Hong Kong plays an important role in its economy. The property prices have been increasing a lot since 2009, which have become a major concern. However, few studies have been done to forecast the property price indices in Hong Kong. In this paper, two grey models, GM(1,1) and GM(0,N), are introduced for the forecasting. The results show that GM(1,1) has a better performance when forecasting with stable trend data, while GM(0,N) is more suitable for forecasting data in fluctuating trend. The sensitivity analysis for GM(0,N) shows that Population(POP) and Best Lending Rate(BLR) are significantly sensitive factors for data in stable trend. While for the fluctuating data, sensitivity of each factor presents uncertainties. This study also compares the forecasting performance of grey models with the ANN model and ARIMA model. The study demonstrates that grey models are more suitable for forecasting the Hong Kong property price indices than others

Utilizing advanced modelling approaches for forecasting air travel demand: a case study of Australia’s domestic low cost carriers

One of the most pervasive trends in the global airline industry over the past few three decades has been the rapid development of low cost carriers (LCCs). Australia has not been immune to this trend. Following deregulation of Australia’s domestic air travel market in the 1990s, a number of LCCs have entered the market, and these carriers have now captured around 31 per cent of the market. Australia’s LCCs require reliable and accurate passenger demand forecasts as part of their fleet, network, and commercial planning and for scaling investments in fleet and their associated infrastructure. Historically, the multiple linear regression (MLR) approach has been the most popular and recommended method for forecasting airline passenger demand. In more recent times, however, new advanced artificial intelligence-based forecasting approaches – artificial neural networks (ANNs), genetic algorithm (GA), and adaptive neuro-fuzzy inference system (ANFIS) - have been applied in a broad range of disciplines. In light of the critical importance of passenger demand forecasts for airline management, as well as the recent developments in artificial intelligence-based forecasting methods, the key aim of this thesis was to specify and empirically examine three artificial intelligence-based approaches (ANNs, GA and ANFIS) as well as the MLR approach, in order to identify the optimum model for forecasting Australia’s domestic LCCs demand. This is the first time that such models – enplaned passengers (PAX) and revenue passenger kilometres performed (RPKs) – have been proposed and tested for forecasting Australia’s domestic LCCs demand. The results show that of the four modeling approaches used in this study that the new, and novel, ANFIS approach provides the most accurate, reliable, and highest predictive capability for forecasting Australia’s LCCs demand. A second aim of the thesis was to explore the principal determinants of Australia’s domestic LCCs demand in order to achieve a greater understanding of the factors which influence air travel demand. The results show that the primary determinants of Australia’s domestic LCCs demand are real best discount airfare, population, real GDP, real GDP per capita, unemployment, world jet fuel prices, real interest rates, and tourism attractiveness. Interestingly three determinants, unemployment, tourism attractiveness, and real interest rates, which have not been empirically examined in any previously reported study of Australia’s domestic LCCs demand, proved to be important predictor variables of Australia’s domestic LCCs demand. The thesis also found that Australia’s LCCs have increasingly embraced a hybrid business model over the past decade. This strategy is similar to LCCs based in other parts of the world. The core outcome of this research, the fact that modelling based on artificial intelligence approaches is far more effective than the traditional models prescribed by the International Civil Aviation Organization (ICAO), means that future work is essential to validate this. From an academic perspective, the modelling presented in this study offers considerable promise for future air travel demand forecasting. The results of this thesis provide new insights into LCCs passenger demand forecasting methods and can assist LCCs executives, airports, aviation consultants, and government agencies with a variety of future planning considerations