Analyze House Price for King County copy.docx

2011

Abstract: Prior literatures on negotiations and auction behavior have both addressed the relationship between initial asking prices and the final sale price of a commodity. However, the recommendations of these two literatures come into direct conflict in the context of a home sale, which possesses features of both an auction and a negotiation.

Comparison of Data Mining Models to Predict House Prices, 2018

Buying a house is commonly the most important financial transaction for the average person. The fact that most prices are negotiated individually (unlike a stock exchange system) creates an environment that results in an inefficient system. Most people buying houses are inexperienced amateurs with limited information about the market. A housing bubble cannot exist if individuals are making rational decisions. The objective of this paper is to evaluate the performance of a stacked regression model compared to several sub models based on predicting house prices. House characteristics and the final house price was gathered from King County, Washington, USA during the period of May 2014 and May 2015. The observed result indicates that combining the sub algorithms using a general linear model did not significantly improve results.

Using data mining techniques to enhance property pricing , 2019

As shown by the financial crash of 2008, the Irish economy is intrinsically linked to the housing market. This paper will attempt to answer if predicting house prices in Dublin is feasible using data mining techniques. Considering both the financial risk accompanied with purchasing a home, and the potential economic implications house prices can have on a nations level of prosperity it's understandable the level of motivation people experience to forecast house prices. Our results clearly show that the variance in house price in Dublin can be explained using data mining techniques. The most important factors are the house characteristics (Location & Size) followed by local amenities. The main limitation is with regard to there being no scientific method to evaluate property quality.

INTERNATIONAL JOURNAL OF ADVANCE RESEARCH, IDEAS AND INNOVATIONS IN TECHNOLOGY

The prices of House increases every year, so there is a need for the system to predict house prices in the future. House price prediction can help the developer to determine the selling price of a house. It also can help the customer to arrange the right time to purchase a house. There are some factors that influence the price of a house which depends on physical conditions, concept, location and others. House prices vary for each place and in different communities. There are various techniques for predicting house prices. One of the efficient ways is by the use of the regression technique. Regression is a reliable method of identifying which variables have an impact on a topic of interest. Random forests are very accurate and robust to over-fitting. The process of performing a regression allows to confidently determine which factors matter the most, which factors can be ignored and how the factors influence each other. The main objective is to use an advanced methodology for prediction.

Finding opportunities in the dynamic property market is a challenging problem. It is needed to establish a model for property buyers (e.g.investors) and then try to recommend appropriate properties to them in real-time. In order to collect a comprehensive data set, I choose zoopla.com(the second biggest UK property website) as data source. A powerful crawling spider is implemented using Scrapy to constantly collect property information on Zoopla. The goal of this project is to find potential houses through machine learning techniques and give recommendations to investors in UK.

2010

This paper studies the effect of a newly completed highway extension on home prices in the surrounding area. We analyze non-linearities in both the effect of distance from the highway and the effect of time relative to the completion of the road segment. While previous studies of the effects of nearby amenities on property and land values have focused on either cross-sectional spatial or temporal patterns, the joint analysis of the two dimensions has not been thoroughly investigated. We use home sale data from a period of 11 years centered around the completion of a new highway extension in metropolitan Los Angeles. We combine a standard hedonic model with a spline regression technique to allow for non-linear variations of the effect along the temporal and spatial dimensions. Our empirical results show that the maximum home price appreciation caused by the new highway extension occurs at moderate distances from the highway after it is completed. Lower price increases for this period are observed for homes sold closer to the highway or much further away. This price pattern gradually fades away in the years following the construction completion. A similar, although weaker, price pattern is also observed in the first years of the construction period. There is no statistically significant distance dependency in the 2 years in our sample prior to the beginning of the construction. This indicates that the housing market is not fully efficient as the information about the impending construction of the highway is not immediately incorporated into sales prices.

Manuscript in progress, 2010

We estimate neighborhood externalities that arise from perceived risk associated with the location of a nearby registered sex offender residence. The 1994 Sexual Offender Act (and 1996 amendments known as Megan's Law) required persons convicted of sex crimes to register their domicile and states to make the information public. Although statutes vary, sex offenders' addresses are publicly available in all states. Employing a simultaneous equation model in which a property's liquidity and selling price are jointly determined, we find that proximity to sexual offender's residence has robust and economically large effects. Results indicate that the presence of a nearby registered sex offender reduces a home's value by approximately 8% (about $13,161 at the mean) and increases time on market by 84% (approximately 92 days), relative to otherwise identical homes. Additionally, we find amplified effects for homes with more bedrooms and if the nearby sex offender is designated by the state as "violent." We interpret the former result to be a proxy for increasing risk aversion among families with more children. Pope for valuable comments on a prior draft. We are indebted to Ed Kinman for invaluable assistance in the use of GIS software and to Lt. William Reed, Jr of the Criminal Justice Information Services Division of the Virginia State Police for providing the historical record of sex offender residences. We would also like to give a special thank you to Velma Zahirovic-Herbert for invaluable help with programming in Stata. Any and all errors, of course, are our own.

This research is dedicated to my beautiful wife and daughter who have exhibited tremendous patience and encouragement as I have spent countless hours away from them working on this degree and thesis.

2008

This paper studies the effect of a newly completed highway extension on home values in the surrounding area. We analyze non-linearities in both the effect of distance from the freeway and the effect of time relative to the completion of the road segment. While previous studies of the effects of nearby amenities on property and land values have focused on either cross-sectional spatial or temporal patterns, the joint analysis of the two dimensions has not been thoroughly investigated. We use home sale data from a period of four years centered around the completion of a new highway extension in metropolitan Los Angeles. We combine a standard hedonic model with a spline regression technique to allow for non-linear variations of the effect along the temporal and spatial dimensions. Our empirical results show that the maximum home price appreciation caused by the new freeway extension occurs at moderate distances from the freeway only after it is completed. Lower price increases for this...

In this study, we use the Hedonic property value method to estimate how a disamenity, bad odor from an open sewer system, affects housing prices in the city of Rawalpindi in Pakistan. We provide estimates of the benefits of converting the open system into a closed sewer system. We find that house rents decrease by approximately 10% if there is an open sewer (nali) by the house. House rents also increase for homes located further away from the main open drain (Nala Lai) -e.g. a house located 400 meters away from the main open drain enjoys a 12 percent increase in rent because of its distance. Sewer smell has a depressing effect on rent in those areas where smell remains constant throughout the day. The results suggest that residents are willing to pay to be away from bad odor emanating from the open sewerage system. City planners need to take this into account and consider installing sewerage pipes in open sewer areas, which would change the nature of Nala Lai from a disamenity to an amenity.