Eric Putnam

Project 06
Python
MIT

SellingPriceEstimator

What should this actually sell for? Depreciation, condition, comparables, and how fast you need it gone.

§ 01
In the browser

What it looks like

SellingPriceEstimator form with category, condition, sale urgency, pricing strategy, brand, and model fields.
The estimate form.
SellingPriceEstimator result showing a recommended list price and price range after calculating.
A calculated price with its recommended range.

§ 02
Overview

How it thinks about price

Most pricing advice for used goods is a single percentage rule applied to everything, which is why a three-year-old laptop and a three-year-old guitar get valued the same way when they should not. This tool uses category-specific depreciation models instead, then adjusts for the condition of the actual item.

The last input is the one people skip: urgency. A price optimized for maximum value and a price optimized for a fast sale are different numbers, so the tool asks which you want and shows you the trade-off rather than hiding it.

§ 03
Features

What it does

  • Category depreciation

    Electronics, musical instruments, auto parts, furniture, sporting goods, and collectibles each get their own model.

  • Condition grading

    Adjusts the estimate across a scale from brand new down to poor.

  • Urgency setting

    Prioritize a quick sale or maximum value, and see what each costs you.

  • Transparent breakdown

    Every calculation factor and the reasoning behind it, so you can argue with the number instead of just accepting it.

  • Comparison research

    Optionally pulls comparison prices from shopping search results when an API key is configured.

  • CLI and web UI

    An interactive command-line interface with colored output, plus a browser-based interface for running estimates and reviewing results.

§ 04
Run it

Getting started

git clone https://github.com/eputnam77/SellingPriceEstimator.git
cd SellingPriceEstimator
python -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate
pip install -r requirements.txt
python main.py

The core estimate works with no configuration. To enable the research features, copy .env.example to .env and set the optional keys:

SERPAPI_API_KEYEnables price comparison research.
SOCIAL_SENTIMENT_API_URLEndpoint for your sentiment provider.
SOCIAL_SENTIMENT_API_KEYAPI key for the sentiment provider.

§ 05
Details

Details

Repositorygithub.com/eputnam77/SellingPriceEstimator
LicenseMIT
InterfacesInteractive CLI and browser-based web UI
External servicesAll optional — the core estimate runs offline
AuthorEric Putnam