PyProForma

A scalable real estate proforma library for Python.

 

PyProForma is a Python library built for real estate, urban planning, and capital planning analysis. Along with my students, I created the library following the Harvard Business School’s Real Estate Analysis Paper.

The library can be found here.

Project Specs:

Duration: 4 months

Development Stage: In Production

Tools:

  • Python

  • GitHub

Deliverables

  • Python Library

Roles

  • Python aDeveloper

 

Overview

Students at the Spitzer School of Architecture’s Masters of Architecture and Urban Design program need to perform a financial analysis of their urban design. To assess the viability of their project, they need to use industry-standard proformas. We analyzed the Harvard Business School essay on performing real estate financial analysis and used the paper as a template for the Python code. The traditional method of this type of analysis limits the user to do one financial computation per Excel file. The Python library allows for economic analysis at scale, enabling students to perform hundreds of financial iterations.

Problems

1. Old method of doing financial analysis on Excel files does not allow for iterative speculations.

2. Cell-driven input in Excel can lead to human prone error.

Solution

1. We built a Python library that allows for n-number of proformas to be generated with no need to launch Numbers, Microsoft Excel, or Google Sheets.

2. The Python library minimizes error by organizing inputs and assumptions around a class. Users can do modifications to any variable in the proforma by modifying subclasses.

 
 
 

Research & Development

 

We built the library over one semester. As a group, we researched the different ways a proforma could be structured. Since the aim of the proforma was to enable students to be able to do an analysis of their projects for NYCHA, we decided to build the most generic version that we could find. Further refinement was done to capture more complex concepts like phasing and Area Median Income (AMI) rental structures.

I coordinated the development of small pieces of the coded and aided students in creating the functions for the library. I built the rest of the classes and data frames around the code developed by students.

A typical real estate financial analysis done in Excel.

Early versions of proforma functions.

Defining inputs for the proforma class

 

Code Base

The library is built around classes that create independent objects that work as an economic engine.

I used a data frame extensively to store the values calculated from the classes. The data frames allow for easy Excel export.

 

Proforma data frame printed on the terminal.

 

Summary

 

As an academic exercise for the SSA students, the experiment of teaching coding using a proforma was successful. Students are using the proforma to analyze the financial performance of their large-scale projects for NYCHA.