US National Automated Valuation Model (AVM) Data

In the rapidly evolving real estate sector, the need for quick, reliable property valuations is more pressing than ever. Traditional methods of property appraisal, while thorough, are time-consuming and subject to human error and bias. This is where our Bulk Automated Valuation Models (AVMs) come into play, providing a tech-driven solution to property valuation that leverages data and algorithms for accuracy and speed.

**What is an AVM?**

Our Bulk Automated Valuation Model (AVM) is a service that uses mathematical modeling to determine current market values. AVMs integrate vast amounts of data, including sales prices, property characteristics, market trends, and geographic information, to estimate real estate values with minimal human intervention – often referred to as “Desktop Valuations”. These models are designed to provide objective and uniform evaluations, helping to standardize property valuations across the board.

**How is an AVM Created?**

Creating an AVM involves several technical and analytical steps:

- Data Collection: AVMs rely on a large dataset that includes historical property prices, features of properties, location details, market conditions, and economic indicators.
- Model Selection: The heart of an AVM is the statistical model used. Common models include multiple regression analysis, machine learning techniques such as decision trees and neural networks, or a combination of these methods. This also includes nightly blind testing, which looks at historical AVMs to update current values.
- Algorithm Training: The selected model is trained with historical data to learn patterns and dynamics of the market.
- Validation and Testing: The model undergoes rigorous validation and testing to ensure its accuracy and reliability, often using new data to test for overfitting and underperformance.
- Deployment: Once tested, the model is deployed as a tool for generating property valuations.

原始信息汇总

US National Automated Valuation Model (AVM) Data 数据集概述

数据集基本信息

• 数据集名称: US National Automated Valuation Model (AVM) Data
• 提供商: The Warren Group
• 试用信息: 30天免费试用
• 数据更新频率: 每周
• 时间覆盖范围: 最近6个月（按周更新）
• 地理覆盖范围: 美国所有州

数据集描述

自动估值模型(AVM)数据提供准确、物业级别的估值，为更智能的贷款、投资和市场决策提供支持。该服务使用数学模型确定当前市场价值，整合大量数据（包括销售价格、物业特征、市场趋势和地理信息），以最少的人工干预估算房地产价值。

业务应用场景

资产估值

快速准确估算住宅和商业物业价值，帮助贷款人、投资者、投资组合经理和房地产平台减少对成本高昂的手动评估的依赖。

市场分析

通过将AVM数据纳入市场研究工作流程，公司可以清晰了解物业价值、定价趋势和地理增值模式。

定价分析

为反映当前和超本地市场现实的动态定价策略提供基础。

风险分析

监控和评估房地产投资组合中的风险敞口，了解市场条件变化如何影响物业估值。

受众细分

根据房屋价值范围、权益水平、社区特征和所有权类型对物业和业主进行分类。

数据字典（AVM_ALL表）

字段列表

• FIPS: Varchar
• PROPERTYID: Varchar
• APN: Varchar
• SITUSFULLSTREETADDRESS: Varchar
• SITUSHOUSENBR: Varchar
• SITUSHOUSENBRSUFFIX: Varchar
• SITUSDIRECTIONLEFT: Varchar
• SITUSSTREET: Varchar
• SITUSMODE: Varchar
• SITUSDIRECTIONRIGHT: Varchar
• SITUSUNITTYPE: Varchar
• SITUSUNITNBR: Varchar
• SITUSCITY: Varchar
• SITUSSTATE: Varchar
• SITUSZIP5: Varchar
• SITUSZIP4: Varchar
• SITUSCARRIERCODE: Varchar
• FINALVALUE: Number
• HIGHVALUE: Number
• LOWVALUE: Number
• CONFIDENCESCORE: Number
• STANDARDDEVIATION: Number
• VALUATIONDATE: Varchar

使用示例

示例1：缅因州巴港的AVM数据

sql SELECT * FROM NATL.AVMHIST WHERE SITUSSTATE = ME AND SITUSCITY = BAR HARBOR

示例2：置信度分数大于98的数据

sql SELECT * FROM NATL.AVMHIST WHERE CONFIDENCESCORE > 98 ORDER BY CONFIDENCESCORE DESC

云区域可用性

• AWS: 支持非洲（开普敦）、亚太（雅加达）、亚太（孟买）、亚太（大阪）等46个地区

法律条款

• 条款类型: 标准条款

提供商信息

The Warren Group是美国领先的全面房地产和抵押贷款数据解决方案提供商，通过战略合作伙伴关系提供各种有价值的数据集。

搜集汇总

背景与挑战

背景概述

该数据集提供美国全国范围的自动化房产估值模型(AVM)服务，通过整合销售价格、房产特征和市场趋势等数据，运用机器学习算法实现快速准确的批量房产估值。AVM采用多阶段建模流程，包括数据收集、模型训练和验证测试，最终生成标准化的客观估值结果。