Mr. Stelios Apostolidis Profile

Stelios Apostolidis

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 21 September 2023 Paper

Developing an automated valuation model for estimating land values in Limassol

Stelios Apostolidis, Thomas Dimopoulos

Proceedings Volume 12786, 127860I (2023) https://doi.org/10.1117/12.2680621

KEYWORDS: Data modeling, Statistical modeling, Databases, Statistical analysis, Roads, Overfitting, Linear regression, Error analysis, Autocorrelation, Statistical methods

Read Abstract +

This study employs the statistical method of Multiple Linear Regression analysis (MLR) to develop an Automated Valuation Model (AVM) for estimating land values by utilizing transaction-based data in Limassol, Cyprus. The authors focus on the confidence level and accuracy of the value estimated by an AVM. Thus, the developed AVM was tested in two contrasting areas of Limassol in terms of location characteristics and market conditions. Most AVMs contain a statistical method to generate the estimated value of a real estate property. However, the outcome of a statistical method is verified by statistical measures. Therefore, if the validation of the predicted value for its accuracy derives from the statistical metrics of the model, then the explanatory variables cannot remain constant. It is implied that the AVM in order to grant the highest statistical metrics for a given property valuation requires different combination of independent variables in different locations, which means that the parameters of the model should change or adjust for every case to obtain the best fit model. The authors demonstrate that the best fit model is obtained when several models are executed with alternative combinations of variables. Hence, the best fit to the regression is given by the model with the better statistical measures when compared to the other models. Consequently, the predicted value is supported by statistical significance and can be adopted at a high confidence level.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years