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In this paper the author investigates the demand for housing characteristics and proximity to local amenities in Perth, Western Australia. By application of hedonic price theory to a sample of house transactions the implicit prices for individual characteristics are estimated and used to estimate inverse demand curves for these characteristics in a second stage analysis. The results confirm the importance of proximity of river, parks, and highways in determining house prices. River views are found to be particularly important being valued at 28% of average selling price. From the inverse demand curves income elasticities of willingness to pay are found to range from 1.0 for age of house to 0.43 for proximity to the river.

"We compute quality-adjusted price indexes for Personal Digital Assistants (PDAs) for the period 1999-2004, using data on prices and characteristics of 203 models sold by 12 manufacturers. The PDA market is growing in size, it is technologically dynamic with very substantial changes in measured characteristics over time, and it has experienced rapid rates of product introduction. Hedonic regressions consistently show prices to be positively related to processor performance, RAM memory, permanent storage capacity, and battery life, as well as several measures of screen size and quality. Features such as networking, biometric identification, camera, and cellphone capability are also positively associated with price. Hedonic price indexes implied by these regressions decline at an AAGR of 21.1% to 25.6% per year during this period. A matched model price index computed from a subset of observations declines at 18.75% per year. Though these PDA rates of price decline are lower than have been estimated for desktop and laptop PCs, consumers in this "ultra-portable" segment of the computer market appear to have enjoyed substantial welfare gains over the past five years"--National Bureau of Economic Research web site

The article puts forward the argument that housing market and property value models currently lack an adequate methodological basis. In order to improve the conceptual framework of the economic analysis of housing market behaviour, necessary institutional parameters need to be included. This, however, causes further difficulties, because some of the institutions are predominantly informal and operate at the cognitive level of market behaviour. While the problem of overstrict assumptions at the aggregate level has been tackled successfully, the next fundamental problem to solve is how to disaggregate feasibly the demand-side analysis. One possibility for this is to generate preference profiles of actors with different institutional backgrounds, using a multicriteria decision technique such as the analytical hierarchy process (AHP).

Stock price analysis through Statistical and Data Science tools: An Overview Preface Stock price analysis involves different methods such as fundamental analysis and technical analysis which is based on data related to price movement of the stock in the past. Price of the stock is affected by various factors such as company's performance, current status of economy and political factor. These factors play an important role in supply and demand of the stock which makes the price to be volatile in the short term. Investors and stock traders aim to book profit through buying and selling the stocks. There are different statistical and data science tools are being used to predict the stock price. Data Science and Statistical tools assume only the stock price's historical data in predicting the future stock price. Statistical tools include measures such as Graph and Charts which depicts the general trend and time series tools such as Auto Regressive Integrated Moving Averages (ARIMA) and regression analysis. Data Science tools include models like Decision Tree, Support Vector Machine (SVM), Artificial Neural Network (ANN) and Long Term and Short Term Memory (LSTM) Models. Current methods include carrying out sentiment analysis of tweets, comments and other social media discussion to extract the hidden sentiment expressed by the users which indicate the positive or negative sentiment towards the stock price and the company. The book provides an overview of the analyzing and predicting stock price movements using statistical and data science tools using R open source software with hypothetical stock data sets. It provides a short introduction to R software to enable the user to understand analysis part in the later part. The book will not go into details of suggesting when to purchase a stock or what at price. The tools presented in the book can be used as a guiding tool in decision making while buying or selling the stock. Vinaitheerthan Renganathan www.vinaitheerthan.com/book.php ISBN: 9789354579738 Link ...

This research was carried out to analyze characteristics influencing cattle prices in Ngalda livestock markets in Yobe State. Fika Local Government was purposively selected based on high concentration of cattle and cattle marketers, Ngalda cattle market has being the major distributing point of the cattle in the state. A total of 130 Buyers were selected using systematic sampling by truncation on weekly basis for period of 26 weeks. Descriptive statistics, Ginni Co-efficient and multiple regressions was employed in analyzing the collected data. The result of the study revealed that 100% of the respondents were male, 47% of cattle buyers were within age group of 31-40years and 81.5% were found to be married. The respondents (66%) were small buyers category, 21.5% medium and 12.3% were large scale buyers. The Ginni co-efficient model shows that the markets structure was competitive with low Ginni co-efficient of 0.474. The regression results indicated colour of the ear, shape of the cattle face and type of horn were the factors that influenced the buyer's preference. Hedonic regression shows that female cattle, big carcass size, short horn cattle and height were found to be statistically significant (P < 0.05), (P < 0.001), (P < 0.05) and (P < 0.001) respectively with positive coefficient across all the models implies that for any unit increase in these variables, buyers would be willing to pay more premium. 1t was therefore recommended that research efforts should target the characteristics of these cattle that buyers are sensitive to so as to enhance profitability production and marketing.