Abstract: This disclosure provides systems, methods, and devices for control and management of production home builders. According to aspects of the disclosure, computerized tools described herein may enable home builders to react more quickly and consistently to new data relating to market forces and home buyer demand. Some features disclosed herein may enable and provide improved performance metric tracking, including dynamically determining a pacing metric based on data. In some aspects, the system may determine a performance pace for a first plurality of homes based on a comparison of home status data for the first plurality of homes to performance data for a second plurality of homes. Such operations may beneficially enable effective pacing of home builder performance that more accurately matches the home sales pace to the home production pace. Additionally, the system may output instructions of actionable steps to improve home production and performance in the market.

Abstract: This disclosure provides systems, methods, and devices for control and management of production home builders. According to aspects, computerized tools described herein may enable home builders to react more quickly and consistently to new data relating to market forces and home buyer demand, enabling the home builder to improve home production and its performance. Some disclosed features may enable and provide improved performance metric tracking or home demand classification, including classifying homes and home builders based on a pacing metric. In some aspects, a system may determine target performance paces for the home builder based on received home status data, such as a completion status related to the construction level of the homes. As such, the disclosed pacing tool may enable home builders to more accurately match the home transactions pace to the home production pace, which may improve marginal gains and provide for greater influence over the housing market.

Abstract: This disclosure provides systems, methods, and devices for control and management of production home builders. According to aspects, computerized tools described herein may enable home builders to react more quickly and consistently to new data relating to market forces and home buyer demand. Some features disclosed herein may enable and provide improved home demand classification, including classifying sets of homes in comparison to other homes in the market. In some aspects, the system receives home data for a first set of homes and a second set of homes, and based on the data, performs a multi-tiered comparison to identify comparable subsets of homes. Such operations may beneficially allow more accurate understanding of value indicators the housing market and how insights to market demand may be captured. Additionally, the system may output instructions to the home builder of actionable steps to improve home production and performance in the market.

Abstract: This disclosure provides systems, methods, and devices for control and management of production home builders. According to aspects, computerized tools described herein may enable home builders to react more quickly and consistently to new data relating to market forces and home buyer demand. Some features enable using trained machine learning (ML) classification and recommendation models to forecast the home buyer market. In some aspects, the system may receive historical home sales data and historical home demand data to extract a set of feature vectors from the received data. The feature vectors may be provided as inputs to a trained ML model to generate a home sales prediction that includes a predicted number of home sales in a future time period for a home builder in a geographic area as well as a predicted change in home sales for a set of homes as compared to a previous time period.

Abstract: This disclosure provides systems, methods, and devices for control and management of production home builders. According to aspects, computerized tools described herein may leverage trained machine learning (ML) models to automatically identify similar homes across a housing market for a more representative price comparison than typical market averages. In some aspects, the system receives a first set of parameter values corresponding to a first home associated with a first entity (e.g., a home builder) to extract a feature vector. The feature vector may be provided to a trained ML model that identifies one or more related homes to the first home using clustering in a multidimensional feature space. The system may determine a target price for the first home based on prices of the identified homes, as well as provide instructions to the home builder of actionable steps to improve home production and performance.