Data Collection: Real Estate

Topics: Real estate, Price, Square foot Pages: 5 (1216 words) Published: July 13, 2009
Data Collection: Real Estate
Data Collection: Real Estate
The first portion of the study focused on identifying the problem, generating the hypothesis that the house in question requires negotiation, and completing a review of the data set. The premise relies on sale price being dependent on the number of bedrooms and bathrooms, square footage, the existence of a garage and pool, the district the home is located and the distance from the city. Table 1.1: Variables Affecting Price

The study will continue by evaluating the data set provided to determine if a 3000 square-foot, six-bedroom house with a garage, pool, 3 baths located 15 miles from the city in district 4 substantiates a $398,900 price tag. The team will review similar research, define the sampling design, compile, and display the data so statistical analysis may follow. Related Research

Research suggests that external and internal factors both affect the sale price of a home. The National Center for Real Estate Research conducted a study using data from 1996 to 2006 for more than 28,800 residential property sales in and around Philadelphia (Bishop, 2004, p. 1). Hedonic regression served as the backbone for the statistical calculations for the study. Major findings of the study are outlined in Table 1.2. Table 1.2: Attributes Effect on Sale Price (Bishop, 2004, p. 2) |Attribute |Effect to price of home | |Basement |Increase 9% | |Additional 1,000 square feet |Increase 3.3% | |In ground swimming pool |Increase 8% | |Additional bathrooms |Increase 10-12% | |Additional bedroom |Increase 4% |

The study indicates that the effect of home features provides underlying data not only to the buyer and seller, but also to realtors and financing institutions.
Rosiers, Lagana, and Theriault review the effect of the size and proximity of primary schools on the sale price of approximately 4300 homes in the Quebec area. The study indicates that previous studies have identified that a close distance to a school decreases the value. Researchers tested the hypotheses with a three-step approach, including a linear equation, a stepwise procedure to identify price determinants, and regression testing to create gamma transformation formulas (Rosiers, Lagana, & Theriault, 2001, p. 154). The findings of the study include larger schools substantiate higher prices, and homes approximately 400 meters from the nearest school will also bring a higher sale price (Rosiers,, 2001, p. 166). Analysts suggest the use of gamma transformation, which provides substantial knowledge to lenders, real estate brokers, and households to understand the effects of other locations on a home’s price.

Pratt discusses the effects of the economy on real estate. Jerry Paine stated, “Buyers have a little more leverage right now, because if the seller doesn’t want to negotiate, the buyer can look somewhere else” (Pratt, 2009, ¶9). Table 1.3 shows the impact of the economy between 2007 and 2008. Table 1.3: Economy’s impact (Pratt, 2009)

|  |2007 |2008 |Percentage | |Homes sold in Wenatchee |879 |624 |Down 29% | |Amount Average Home |$271,522 |$270,711 |Down less than 1% | |Median Sale Price |$244,500 |$236,750 |Down 3% |

Bond, Seiler, and Seiler’s...

References: Bond, M.T., Seiler, V.L., & Seiler, M.J. (2002). Residential real estate prices: A room with a view. Journal of Real Estate Research, 23, 129-138. Retrieved on January 19, 2009, from ProQuest database.
Pratt, C. (2009, January 12). Homes still selling despite slump. McClatchy – Tribune Business News. Retrieved on January 19, 2009, from ProQuest database.
Rosiers, F.D., Lagana, A., & Theriault, M. (2001, Jun). Size and proximity effects of primary schools on surrounding house values. Journal of Property Research, (18)2, 149. Retrieved on January 21, 2009, from EBSCOhost.
Sekaran, U. (2003). Research methods for business: A skill-building approach (4th ed.). New York: Wiley, Inc.
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