Documentation

How Resivane Models Renovation ROI

Understand the methodology behind our renovation cost estimation, resale impact modeling, and Monte Carlo simulation.

API Endpoints

POST
/v1/calculate

Calculate renovation ROI for a single project

POST
/v1/compare

Compare ROI across multiple renovation projects

GET
/v1/costs/:zip/:projectType

Get local cost estimates for a project type

GET
/v1/resale/:zip/:projectType

Get resale value impact data

POST
/v1/simulate

Run Monte Carlo simulation with custom parameters

Cost Estimation Methodology

Resivane estimates renovation costs using a multi-source approach. We start with national average costs from RSMeans construction cost data and NAHB Housing Economics, then apply local labor rate adjustments from Bureau of Labor Statistics OES data and regional material cost indices. The result is a ZIP-code-specific cost range that reflects what homeowners in your area actually pay.

Each cost estimate includes a confidence interval derived from the standard deviation across data sources. When contractor bids from HomeAdvisor and Houzz fall within our modeled range, it validates our estimates. When they diverge, we flag the discrepancy so you can investigate further.

Data Sources

NAHB

National Association of Home Builders -- construction cost indices and housing economics data.

NAR Remodeling Impact

National Association of Realtors -- renovation project resale value recovery rates by project type.

RSMeans

Gordian RSMeans -- localized construction cost benchmarks for materials and labor.

FHFA HPI

Federal Housing Finance Agency -- metro-level home price appreciation for resale projections.