Automated Valuation Model (AVM) API for individual homes valuation

As a specialist in the field of model-based valuations we have developed an Automated Valuation Model (AVM) in-house. Our AVM is trained to determine current house values of one or more real estate objects. Machine Learning (ML) technology is used for this, which is fed with various information about the object to be valued (such as year of construction, living area, plot area, energy label and housing type), plus location characteristics, neighborhood data, market developments and historical transaction prices of reference houses throughout the Netherlands. The data we use for this go back to 1995. Quality Assurance When calculating a house value our AVM assumes normal sales conditions. For example, only private transactions are considered. Details such as family transactions, foreclosure sales or other outliers that could cast doubt on the representativeness of a house value are not included in our model. To guarantee the very high quality of our AVM it is necessary that the model is continuously provided with current and reliable data. We achieve this by ‘retraining’ our model monthly with the latest real estate data. To do so, we randomly split our dataset into a training and validation set. The validation set is used after training to avoid any sample bias. We compare the predictions to the actual transaction prices and calculate various statistics such as the mean, the median deviation and the spread coefficient. Audit Erasmus University In addition to the monthly internal test we are annually tested by Erasmus University. We have this independent assessment carried out to ensure the highest level of quality of our AVM. Besides, we think it is important that customers can trust us and we believe that transparency and verifiability are essential indicators for this. ISAE 3402 type II certificate We are currently also independently tested by an external auditor. In all likelihood we will obtain the ISAE3402 Type II certificate January 2022. With this we want to confirm to our customers that the model values that they outsource to us are in good hands. Benefits: - Efficient mortgage provision - Effective Marketing and customer retention - Fraud detection and risk reporting - Portfolio analyses - Efficient and extra test on physical appraisals - Assessment of received WOZ values - Highly accurate valuations of individual houses - Based on most recent transactions of reference homes - Complete overview of house information - In accordance with the NRVT regulations - Saves time and money - Available in your own corporate identity

搜集汇总

背景与挑战

背景概述

该数据集提供了一个用于荷兰独栋房屋估值的自动估值模型API，基于机器学习技术，利用房屋特征、位置信息和历史交易数据（可追溯至1995年）进行训练。模型每月更新并通过内部验证与外部审计确保高质量，符合NRVT法规，旨在提供高效、准确的估值服务，支持抵押贷款、风险分析等应用。