Valuation Analysis of Commercial Real Estate Using the Nearest Neighbors Appraisal Technique

Isakson, Hans R.

doi:10.1111/j.1468-2257.1988.tb00466.x

Cited by 15 publications

(9 citation statements)

References 3 publications

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“…The most known are the k-nearest neighbors, where the investigated variablethe priceis the average of the values that the variable assumes in the number k of the closest cases. Isakson (1988) uses the technique to predict the value of 143 real estate properties in Dallas divided between apartment, industrial, office and retail. In all four types the nearest neighbors achieve better performance than the OLS (Ordinary Least Squares) method.…”

Section: General Literature Reviewmentioning

confidence: 99%

Who performs better? AVMs vs hedonic models

Valier

2020

JPIF

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PurposeIn the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.Design/methodology/approachAll tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.FindingsMachine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities.Practical implicationsAVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical.Originality/valueAccording to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.

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Section: General Literature Reviewmentioning

confidence: 99%

Who performs better? AVMs vs hedonic models

Valier

2020

JPIF

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“…Among them, the k-nearest neighbors. Such algorithms have rarely proved effective [ 17 , 18 ]. In contrast, support vector machines have been shown to provide reliable estimates [ 19 ].…”

Section: Frameworkmentioning

confidence: 99%

The Cross Validation in Automated Valuation Models: A Proposal for Use

Valier

2020

Lecture Notes in Computer Science

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The appraisal of large amounts of properties is often entrusted to Automated Valuation Models (AVM). At one time, only econometric models were used for this purpose. More recently, also machine learning models are used in mass appraisal techniques. The literature has devoted much attention to assessing the performance capabilities of these models. Verification tests first train a model on a training set, then measure the prediction error of the model on a set of data not met before: the testing set. The prediction error is measured with an accuracy indicator. However, verification on the testing set alone may be insufficient to describe the model’s performance. In addition, it may not detect the existence of model bias such as overfitting. This research proposes the use of cross validation to provide a more complete and effective evaluation of models. Ten-fold cross validation is used within 5 models (linear regression, regression tree, random forest, nearest neighbors, multilayer perception) in the assessment of 1,400 properties in the city of Turin. The results obtained during validation provide additional information for the evaluation of the models. This information cannot be provided by the accuracy measurement when considered alone.

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“…Some interesting academic studies focused on the sales comparison approach with greater detail. Isakson (1986Isakson ( , 1988 presented a technique called the nearest neighbors appraisal technique (NNAT) according to which the final estimate of value is calculated as a weighted average of the actual selling prices of the comparable properties. The weights in NNAT are determined by a multidimensional measure of similarity, so that comparable properties more similar to the subject have greater weight.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In both techniques, the adjusted values of the comparable properties were calculated using the dollar additive technique and OLS-derived adjustment factors. Isakson's (1986) NNAT, Vandell's minimum variance, and Gau, Lai, and Wang's minimum coefficient of variation are computationally complex and their techniques have not been given much attention in professional work for this reason. Pace (1998) presented the sales comparison approach in a mathematical format (matrix algebra).…”

Section: Literature Reviewmentioning

confidence: 99%

Measurements of Rationality for a Scientific Approach to the Market-Oriented Methods

Simonotti

Salvo

Ciuna³

et al. 2016

Journal of Real Estate Literature

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Appraisals are based on regression models. Its major application limits are all in situations in which the real estate market doesn't offer a sufficient number of comparables to implement statistical tools. In low-transaction real estate markets, the most effective appraisal method is the market comparison approach (MCA). The application limits of MCA depend on the degree of similarity between the properties' characteristics and the reliability of the prices of comparables. In this paper, we introduce some rational measurements in order to guarantee a scientific approach to MCA is associated with the correct prices. Our method is able to restore appraisals of the market value of any type of property, even in situations in which the comparables are limited or when the reliability of the buying and selling prices could be better.The International Valuation Standards (IVS) are internationally recognized appraisal methods used in the market-oriented approach, the income approach, and the cost approach. When a real estate market is active and all necessary market data are available, the market comparison approach (MCA) is the most direct, probative, and documented method used to appraise real estate market values (Simonotti, 2006); in particular, the MCA is the most important method and is comparable to the marketoriented approach. The MCA is known by several different names in the appraisal literature. In some of the older literature it is called the market data approach, while elsewhere it is referred to as the grid adjustment technique.There are many appraisal procedures that can be used in the logic application of the MCA. These procedures are grouped into two categories: (1) quantitative techniques based on detection of objective markets data (paired data analysis, graphic analysis, trend analysis, secondary data analysis, adjustment grid method, etc.); and (2) qualitative techniques based on the analyst's subjective judgment (relative comparison analysis, personal interviews, etc.).

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Valuation Analysis of Commercial Real Estate Using the Nearest Neighbors Appraisal Technique

Cited by 15 publications

References 3 publications

Who performs better? AVMs vs hedonic models

Who performs better? AVMs vs hedonic models

The Cross Validation in Automated Valuation Models: A Proposal for Use

Measurements of Rationality for a Scientific Approach to the Market-Oriented Methods

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