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Relationship of List to Sale Price in Real Estate

Autor:   •  November 18, 2018  •  Case Study  •  1,970 Words (8 Pages)  •  68 Views

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Relationship of List to Sale Price in Real Estate

Daniel Hall

Davenport University

STAT220 – Introduction to Statistics

Ghada Khoury

February 20, 2017


Abstract

Writing an offer to purchase real estate is a stressful situation made more stressful by the ambiguity of home sale prices.  Offer too little and you could lose the home of your dreams, offer too much and you could end up paying more than you necessary to acquire the home.  Most people rely on the skills of a real estate sales professional, but what if there were another way?  This paper examines statistics, specifically, the linear regression equation to calculate a formula that when used with sufficient data will produce an offer price having a high likelihood of being accepted by a seller.  


Introduction

One of the most stressful activities people face is buying a home.  It is widely believed to be the largest investment most people will make in their lifetimes.  One of the most stressful aspects of making a home purchase is deciding on the offer price.  The prevailing belief being, set your offer price to high, and you pay too much, too low and you may lose the home of your dream.  So what do you do?  Most people rely on a real estate sales person to assist them in this task.  However, if you ask ten real estate salespersons how much an individual should offer on a home listed for sale you may get ten different answers.

Why? The listing or asking price advertised for any residential real estate offering is, according to Horowitz, (1992) an announced price (p. 1.). Certainly, the seller is not expecting to receive a sale price higher than the asking price.  Per author Horowitz (1992) homes are frequently sold at prices less than asking price but rarely if ever above asking price (p.1). This paper examines the list price, sold price (quantitative variables) and residential (categorical variable) of home sales that occurred in the city of Grand Rapids, Michigan between February 2011 and June 2016 to quantify the relationship between list price and sale price.

Data

First, it must be determined if the data (see Appendix I) I have obtained is usable (i.e. are there any errors in the data set, are there outliers, and is there stable variation). Determining if there are outliers is done using the upper and lower limit calculations for outliers (see Figure 1). No values for either List Price or Price Sold fall outside of these limits. Thus, there are no outliers in my dataset. One can also see this visually by using the below Box Plot chart for List Price and Price Sold.

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