Sales Comparison Approach

This approach, like the name implies, entails comparing your property to the sale of comparable properties in your market area. Adjusting for differences (such as numbers of bathrooms) by adding (or subtracting) the approximate value of the differences to the sale price, you can get an indication of what your property might sell for.

is the value of the property as an investment, and therefore is also the most probable selling price.

A closer scrutiny of the market was done by pulling three sales of homes closely comparable to the subject in terms of overall quality grade and the size of above grade living area (see County sales grid). The comps were sold in the range of $770,000 to $900,000. The subject is relatively inferior to comp #1 in terms of size of above grade living area and superior to comp #2 in terms of overall quality grade.

The boxplots below show the real estate values of single family homes in two neighboring cities,

The mean, median, and range value estimates for COSTVALU and MKTVALU are higher than ASSESVAL. The statistics for last year’s value estimate (ASSESVAL) are lower than this year’s values estimates (COSTVALU and MKTVALU). The difference may be from an increase in market values over the past year. The direct comparison approach produces the most uniform estimate.

The real estate division was estimated to have a fair value of $13,890,000. This was determined by totaling the number of lots expected to sell within the next four years and multiplying it by the price per lot of $180,000. After determining total lot sales, a 20% discount rate was applied as suggested by current market conditions. Given the unique nature of the real estate development, it is not believed that there are any comparable developments to find a market multiple.

The cited comparables are similar in overall quality of constriction. They are newer homes, no age adjustment was required due to the extensive renovation of the subject property; the effective age of the subject and the comparables is similar. Comparables #1 and #2 are are most similar in overall condition, like the subject property, they had interior renovations. Comparable #3 is located in Whispering Pines neighborehood. Some floor converings and light fixtures have been replaced in comparable #3; however, this home is still inferior in overall condition. Comparables #1 and #2 are located in competing neighborhoods. Comparable #1 like the subject has a driveway only. This home has an open patio. Comparable #2 has a covered and an open deck,

Using initial rates also allows one to examine the effects of choice on property values

This case involves an investigation of the factors that affect the sale price of Oceanside condominium units. It represents an extension of an analysis of the same data by Herman Kelting (1979). Although condo sale prices have increased dramatically over the past 20 years, the relationship between these factors and sale price remain about the same. Consequently, the data provide valuable insight into today’s condominium sales market.

This case investigates the factors that are affecting the sale price of Oceanside condominium units. The relationship between these factors and sale price has remained the same despite condo sale prices increasing drastically over the past 20 years.

First, we found that the average (mean) figures for the Gulf View condominiums are: List price $537,080, Sell price $515,800, days to sell 186.65. From these figures and the data provided, we can calculate outliers of the Interquartile range. The Interquartile range is a series of figures taken from data that are clustered to a central figure, the mean (Elizabeth Staple, 2015). The median for the list price is $480,000, the first quartile figure is $331,000 and the third quartile is $666,000. The first and third

Automated valuation models (AVM) according to the RICS AVM Standards working group are systems that use one or more mathematical techniques to provide an estimate of the value of a specified property at a specified date, accompanied by a measure of confidence in the accuracy of the result, without human intervention post-initiation. They combine property sales data, property attributes data as well as local market information (RICS 2013, Corelogic (n.d)); these form the variables that are fed into the model. Models typically comprise one dependent variable which is the estimated property value and several independent variables (property attributes data) which take turns in explaining the dependent variable (RICS, 2013). AVMs vary depending on the modelling technique adopted, the methodology and independent variables adopted. Choice is solely down to the provider’s specification (RICS, 2013). Examples of the different models include; multiple regression model, indexation, sales comparison models and automated comparable selection and artificial neural networks. AVMs have been around for a while. However, market acceptance has been slow, tentative and somewhat phased.

The data for the second test to be conducted by our group consists of lot sizes of the residential properties that are up for sale in Toronto and Vancouver. The samples are represented in m2 (metres squared; area of the land in which the residential properties are built on). The data taken are based on the properties that are up for

A difficult characteristic to understand about the housing market is how a price is given for a particular house. That price will be designated to that particular house alone. All houses have various pricing, so I can’t always assume that one will cost more or less than any other. The pricing for houses vary based on their characteristics. Each characteristic must be analyzed to determine its contribution or detraction toward the price. I have taken some of these characteristics and modeled the relationship between them and the price of real estate for a specific area.

‘Modelling the Value-Adding Attributes of Real Estate to the Wealth Maximization of the Firm.’ This paper was authored by Anna-Liisa Lindholm (Helsinki University of Technology, Finland), Karen M. Gibler (Georgia State University, Atlanta, GA) and Kari I. Leväinen (Helsinki University of Technology, Finland). It was published in December, 2009 in the Journal of Real Estate Society Vol. 28 by the American Publisher.