How Is the Audubon Housing Market Changing?

Audubon, New Jersey | Residential Property Market Analysis

By Mustafa Saqib | August, 2026

Project Overview

The Audubon Housing Market Analysis examines how residential property sales and home prices in Audubon, New Jersey, have changed over time.

Using publicly available property-transfer data, the project analyzes sales volume, sale prices, assessed and fair market values, property characteristics, property age, and recent market activity to identify how the local housing market has evolved.

The analysis uses a structured data-analysis approach to transform raw public records into meaningful insights about a local housing market.

Research Question

What is happening to home prices and property sales in Audubon?

The analysis examines several dimensions of the local housing market:

How many residential properties were sold each year?
How have median sale prices changed over time?
How has the range of sale prices changed?
What types and ages of homes are being sold?
How do assessed and fair market values compare with sale prices?
Has the Audubon housing market slowed or accelerated since 2021?

Why This Question Matters

Housing markets affect homeowners, buyers, investors, local governments, and the broader community.

Understanding changes in property sales and prices can help identify whether a local market is growing, slowing, or becoming more expensive.

For a municipality such as Audubon, this type of analysis can provide useful evidence for understanding neighborhood change, property values, housing conditions, and recent market activity.

More broadly, the project demonstrates how publicly available local government data can be transformed into evidence that supports data-informed decision-making.

Data Source

This analysis uses publicly available property transaction records from the New Jersey Division of Taxation's SR1A Sales File. The SR1A files provide property transaction and assessment-related information used by New Jersey for its assessment-sales ratio and property-tax analysis.

For this case study, annual New Jersey property transaction files covering 2019–2025 were processed to identify residential property sales in Audubon, New Jersey. The records were cleaned and analyzed to examine sales volume, sale prices, assessed values, price distributions, and property characteristics.

Source: New Jersey Division of Taxation, Local Property Tax — SR1A Sales File. https://www.nj.gov/treasury/taxation/lpt/statdata.shtml

Study Area: Audubon, New Jersey
Study Period: 2019–2025

Tools

Google Sheets / Spreadsheets — Data organization, calculations, summaries, and visualization
Python (Pandas) — Data cleaning, transformation, filtering, and analysis
SQL — Data querying and aggregation
Tableau — Data visualization and presentation of findings

Methodology

The project follows the Google Data Analytics framework: Ask → Prepare → Process → Analyze → Share → Action

Ask

The analytical goal was to determine how Audubon's residential property market changed over time, with particular attention to transaction volume, prices, assessed values, market distribution, and property characteristics.

Prepare

The analysis uses New Jersey property transaction records covering the study period 2019–2025. The working dataset was assembled from annual sales records, including the historical records available in the source data and annual files for 2020 through 2025.

Process

Python was used to read and parse fixed-width text files, extract sale dates and prices, extract assessed values, identify property classes, identify usable residential sales, combine annual records, clean missing or invalid values, create analytical variables, and export the cleaned dataset to CSV. Google Sheets was then used for exploratory analysis, summary tables, formulas, and visualization. Tableau is planned for the final interactive dashboard.

The analysis uses annual counts, medians, minimums, maximums, ratios, and grouped categories. Median values were emphasized for prices and assessed values because medians are less sensitive than means to unusually high or low transactions.

Analyze

Findings are presented through a written report and a presentation designed for a data-analytics portfolio. Charts emphasize trend comparisons and make the distinction between sales volume and prices visible.

The findings were presented through charts, summary tables, a written report, and a presentation. The visualizations focused on annual changes in sales volume, sale prices, assessed values, and property characteristics, with the results organized to communicate the analysis clearly to a non-technical audience. Tableau will also be used to develop an interactive dashboard to present the findings and allow users to explore the data visually.

Action

The completed analysis produced a cleaned dataset and a set of findings that can be used for further analysis of housing sales, assessed values, property characteristics, and market trends. The project also identified opportunities for additional visualization and exploration, including the development of an interactive Tableau dashboard.