House Price Prediction: Hedonic Price Model vs. Artificial Neural Network

The objective of this paper is to empirically compare the predictive power of the hedonic model with an artificial neural network model on house price prediction. A sample of 200 houses in Christchurch, New Zealand is randomly selected from the Harcourt website. Factors including house size, house age, house type, number of bedrooms, number of bathrooms, number of garages, amenities around the house and geographical location are considered. Empirical results support the potential of artificial neural network on house price prediction, although previous studies have commented on its black box nature and achieved different conclusions.Hedonic Model, Artificial Neural Network (ANN), House Price., Environmental Economics and Policy, Land Economics/Use, Research Methods/ Statistical Methods, C53, L74,

Factors Affecting Pure Orange Juice Purchasing Decisions of Consumers

This paper analyzes factors affecting pure orange juice purchasing decisions of consumers in Bangkok Metropolitan area. The data used in this research were from interviewing 400 consumers in Bangkok Metropolitan area who used to buy pure orange juice. The data were collected during September to October 2008. The descriptive analysis techniques and Conjoint Analysis were applied. The results showed that certificate of standard and quality assurance was the most important factor which influences pure orange juice purchasing decisions of consumers followed by nutrition and price factors, respectively. Types of oranges and packaging were relatively insignificant factors affecting the consumer’s decisions.Orange Juice, Conjoint Analysis, Consumers’ Preferences

An Analysis of Credit Scoring Model for Rural Financial Market in Thailand.

Loan contracts performance determines the profitability and stability of the financial institutions. The screening process of the loan applications is a key process in minimizing credit risk. Thus, the purpose of this research is to develop credit scoring model for rural financial market in Thailand. The results verify the important of asset value, capital turnover ratio, and the duration of bank-borrower relationship as important factors in determining the probability of a good loan in agricultural lending, whereas return on asset and capital turnover ratio are key factors in determining the probability of a good loan in non-agricultural lending. The study supports the use of Probabilistic Neural Network (PNN) in classifying good and bad loans. It is found that the PNN can detect a bad loan more accurately than Logit and Artificial Neural Network (ANN) models, and it gives the lowest misclassification costs.Credit Scoring, Rural Financial Market

House Price Prediction: Hedonic Price Model vs. Artificial Neural Network

The objective of this paper is to empirically compare the predictive power of the hedonic model with an artificial neural network model on house price prediction. A sample of 200 houses in Christchurch, New Zealand is randomly selected from the Harcourt website. Factors including house size, house age, house type, number of bedrooms, number of bathrooms, number of garages, amenities around the house and geographical location are considered. Empirical results support the potential of artificial neural network on house price prediction, although previous studies have commented on its black box nature and achieved different conclusions

Rural financing in Thailand

Rural financing in Thailand is heavily dependent on bank lending. Therefore, understanding the determinants of bank lending in the rural sector is an important element for promoting the development of credit accessibility to Thai farmers in the rural regions. Appropriate bank lending decisions would reduce lending costs and increase repayment rate and profits to the banks. Thus, a well-developed rural financial market would lead to sustainable development in the rural sector. The purpose of this research is to identify critical factors in the bank lending decision and to investigate what factors affect the credit availability and loan price in rural lending in Thailand. This research also investigates the impact of the relationship lending (i.e., the relationship between the bank and the borrower) and the predictive power among the different estimation techniques in predicting the bank lending decision, amount of credit granted, and interest rate charged. The data used in this research are obtained from the Bank for Agriculture and Agricultural Cooperative (BAAC). During the period of 2001 to 2003, a total of 18,798 credit files under the normal loan scheme are made available. The credit files are analyzed using the logistic regression (Logit), multiple linear regression (MLR), and four different types of the artificial neural networks (ANN), namely multi-layer feed-forward neural networks (MLFN), Ward networks (WD), general regression neural networks (GRNN), and probabilistic neural networks (PNN). The results show that the total asset value (Asset), value of collateral (Collateral), and the length of the bank-borrower relationship (Duration) are crucial factors in determining bank lending decision, amount of credit granted, and interest rate charged. As expected, Asset has a positive impact on the bank lending decision and the amount of credit granted, while Collateral has a positive and a negative influence on the amount of credit granted and the interest rate charged, respectively. However, Collateral has no significant impact on the bank lending decision, while Asset has a significant negative impact on the loan price in some specifications. Duration has a significant negative impact on bank lending decision, amount of credit granted, and interest rate charged, which implies the importance of relationship lending in the Thailand rural financial market. However, the negative relationships between Duration and the bank lending decision, and between Duration and the amount of credit granted, contradict the postulated hypothesizes. The results imply that the bank uses information from the borrowers and monitors the lending risk via the lending decision and amount of credit granted. On the other hand, the relationship lending benefits the borrowers via loan pricing since the borrowers with a long term relationship with the bank receive a lower lending rate. The predictive results of both in-sample and out-of-sample on bank lending decision, amount of credit granted, and interest rate charged show that in terms of predictive accuracy, most of the artificial neural networks models outperform the logistic and the multiple regression models. The empirical results also show the superiority of using the PNN model to classify and screen the loan applications, and the GRNN model to determine the amount of credit granted and interest rate charged

An analysis of credit scoring for agricultural loans in Thailand

The following is a summary of An Analysis of Credit Scoring for Agricultural Loans in Thailand written by Visit Limsombunchai, Christopher Gan and Minsoo Lee, published by Science Publications in 2005. The purpose of the summarized study is to determine the optimal credit-scoring model for agricultural loans in Thailand. Three credit scoring models are tested to predict a borrower’s creditworthiness and default risk

An analysis of credit scoring for agricultural loans in Thailand

Loan contract performance determines the profitability and stability of the financial institutions and screening the loan applications is a key process in minimizing credit risk. Before making any credit decisions, credit analysis (the assessment of the financial history and financial backgrounds of the borrowers) should be completed as part of the screening process. A good credit risk assessment assists financial institutions on loan pricing, determining amount of credit, credit risk management, reduction of default risk and increase in debt repayment. The purpose of this study is to estimate a credit scoring model for the agricultural loans in Thailand. The logistic regression and Artificial Neural Networks (ANN) are used to construct the credit scoring models and to predict the borrower’s creditworthiness and default risk. The results of the logistic regression confirm the importance of total asset value, capital turnover ratio (efficiency) and the duration of a bank - borrower relationship as important factors in determining the creditworthiness of the borrowers. The results also show that a higher value of assets implies a higher credit worthiness and a higher probability of a good loan. However, the negative signs found on both capital turnover ratio and the duration of bank borrower relationship, which contradict with the hypothesized signs, suggest that the borrower who has a long relationship with the bank and who has a higher gross income to total assets has a higher probability to default on debt repayment

A logit analysis of electronic banking in New Zealand

Financial institutions have been adopting internet banking since the mid 90s, predominantly due to lower operating costs associated with internet banking, and pressure from non-banks interested in entering the internet banking market. In addition, customers utilizing internet banking facilities are increasing, as the cost savings on transactions over the internet are substantial (Mols, 1998; Sathye, 1999). Internet banking enables speedy transactions, access, time and money savings through providing free paper, and complete and up-to-date transactions. The competitive landscape of financial institutions is shifting as internet banking is no longer a competitive advantage but a competitive necessity for banks.The literature has featured numerous published research papers, articles and books addressing a wide range of issues relating to electronic banking (see Pyun, Scruggs and Nam, 2002; Li, 2002; Mols, 1999). However, there is little empirical research on the effect of electronic channels on consumer's buying behaviour (Hendrikse and Christiaanse, 2000) or banking channel preferences in New Zealand.The purpose of this research is to examine consumers' decision-making between electronic banking and non-electronic banking in New Zealand. The research uses the consumer decision making process (or paradigm) to identify factors that consumers use when deciding between electronic banking and non-electronic banking. These factors include service quality dimensions, perceived risk factors, user input factors, price factors, service product characteristics, and individual factors. The demographic variables include age, gender, marital status, ethnic background, educational qualification, employment, income, and area of residence

The Operation and Management of Village Development Fund in Champasak Province, Lao PDR

As a least developed country, the economy of Lao PDR is agricultural based with high percentage of poor people living in rural area, poverty eradication and rural development is at the centre of the government’s economic and social development policies. Village development fund has been used as an important strategy to increase access to financial capital of the rural poor. This paper reviews the development of village development fund in Champasak province, Lao PDR and assess its operation and management especially on the structure, management rules, operation and performance aspects, and supported by the interview of village development fund management committees of two districts in the province. The results show that the village development fund has expanded gradually over the past few years. The management is generally satisfactory. However, main problems encountered are those related to ethical ground and good governance of management personnel. By policies, the village development fund shall contribute to social development in the villages. This requires not only management skills and experiences to optimize business and social goals, but also knowledge and understanding of the members to accpet flexible or relative moderate rate of return to deposit. The non-performance loans, as observed, are mostly caused by crop or livestock raising failure. Hence, to sustain the village development fund in Champasak province, it is important to widen the capacity development by more campaign on basic knowledge of principles/concepts among the members; ethical development and good governance among management and advisory committees. To avoid non-performance loan, it is essential to reduce risks from investment in agricultural or non-agricultural activities. This requires more support from the public sector to ensure efficiency and sustainability of the village development fund