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Competing with E-Commerce in the Real Estate Industry: A Status Report by Waleed A. Muhanna, March, 2000

In a very short time, the Internet has emerged as a viable commercial medium, forcing significant changes upon industry. This paper summarizes the results of a survey of 150 Ohio residential real estate firms designed to examine how they are attempting to adapt to the new medium and assess their perceptions regarding its potential implications. Results indicate, among others, a dramatic (50%) rise in the number of firms using the web channel during the second and third quarter of 1999, and that the investments in web technology are paying off for the vast majority of firms. Further, it appears that the impetus behind the push to adopt the new technology stems not from a fear of losing business to others, but largely from a desire to leverage the new medium to attract new buyers and reduce marketing and customer acquisition costs. Firms overwhelmingly view the Internet as an opportunity as opposed to a threat. At the same time, respondents report that they expect a very substantial increase in the amount of business coming through the new channel by 2002. This raises the possibility that firms may be underestimating the potential of the new medium to threaten the existing order and reshape the real estate industry.

Climbing the Housing Ladder: Repeat Homebuyers and Their Satisfaction by Hazel A. Morrow-Jones and David A. Lipsetz, June, 2000

This research combined deed transfer records with survey results for Franklin County, Ohio in 1995 to analyze the satisfaction levels of repeat homebuyers with their new homes. The report set the context by comparing the characteristics of the Franklin County sample to a National Association of Realtors 1995 national study of repeat home buyers and finds that on the whole the two are similar. Franklin County buyers are slightly less well off and buy slightly less expensive homes than the national group. They are also slightly more likely to be in either younger or older age groups than the national averages. Franklin County homebuyers appear to be more concerned with the investment value of their housing, but the survey questions are not directly comparable, so this should be taken as suggestive rather than definitive.
The 2,420 repeat homebuyers in the population moved in patterns that showed a significant loss of these households in the central city school district, a marginal loss in the inner suburban school districts and a significant gain in the outer suburban school districts. Eight hundred thirty-nine surveys were returned (out of 1,000 mailed) from this population and approximately 75% of the homebuyers agreed with the statement that their new home was better than their former home.
Those who were less happy with their new homes were more likely to be single and to have moved down in size or price. These characteristics probably reflect life cycle oriented moves as older people or newly singled people move to housing they can afford or maintain more easily. It is not surprising that they would feel that the new house is not as good, because by many objective measures it is not. The monograph discusses the problem that people could feel that the new house is not as good as the old one, but still be glad they moved, and thus satisfied with the move. Future survey design will test this possibility.
An analysis of reasons for moving also indicates that those who moved for larger, more expensive and newer homes were more likely to be satisfied after the move. This also fits with the life cycle explanation mentioned above.
We developed a set of OLS regression models to attempt to predict satisfaction with the home based on:
Model 1: Characteristics of the house sold
Model 2: Characteristics of the house sold and the household
Model 3: Characteristics of the house sold, the household and the reasons for selling
Model 4: All characteristics from models 1-3 plus differences between the house bought and the house sold
Model 5: All characteristics from models 1-4 plus characteristics of the home purchased
Model 6: All characteristics from models 1-5 plus reasons for choosing the new home and location
The predictive power of these models increased monotonically with similar variables remaining important throughout. The coefficients of determination (adjusted for sample size) ranged from .054 to .318 over the six models.
Future research will extend the modeling effort to the seven county central Ohio area for 1998 and use an improved questionnaire. In particular, the issue of moving to a lower quality home but being satisfied with the move will be explored.

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