Learning Python Fundamentals for Real Estate Bootcamp

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Free Download Python Fundamentals for Real Estate Bootcamp
Published 5/2026
Created by Bayfield Training
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 70 Lectures ( 2h 50m ) | Size: 1.1 GB

Python for real estate: automate analysis, build DCF models, and run advanced sensitivity beyond Excel
What you'll learn
Set up and code in Python using Anaconda and Jupyter, applying core concepts like variables, data types, loops, and operators.
Work with key libraries (NumPy, Pandas, Matplotlib, Seaborn) to clean, analyse, and visualise real estate datasets.
Build property cash flow models in Python, calculating NPV, IRR, and testing investment viability with and without leverage.
Run sensitivity analyses (simple and multi-dimensional) to evaluate how changes in assumptions affect investment outcomes.
Integrate Python with Excel using xlwings to automate workflows, extend existing models, and export results for professional reports.
Requirements
No previous experience required. This is a beginner-level course,
Description
The objective of this course is to equip real estate professionals with practical, hands-on skills in Python programming for data analysis, financial modelling, and Excel integration.
By the end of the course, participants will be able to apply Python to automate workflows, analyse large property datasets, build discounted cash flow (DCF) models, and conduct advanced sensitivity analyses that go beyond Excel's limitations.
The course emphasises both fundamental coding concepts and their direct application to real estate investment analysis and decision-making
On this course, you will.
Set up and code in Python using Anaconda and Jupyter, applying core concepts like variables, data types, loops, and operators.
Work with key libraries(NumPy, Pandas, Matplotlib, Seaborn) to clean, analyse, and visualise real estate datasets.
Build property cash flow models in Python, calculating NPV, IRR, and testing investment viability with and without leverage.
Run sensitivity analyses (simple and multi-dimensional) to evaluate how changes in assumptions affect investment outcomes.
Integrate Python with Excel using xlwings to automate workflows, extend existing models, and export results for professional reports.
Who will benefit from this course?
Real Estate Analysts who want to move beyond Excel into Python for faster, more advanced financial modelling, cash flow analysis, and sensitivity testing.
Investment Managers & Asset Managers seeking to integrate Python with existing workflows to streamline reporting, automate repetitive tasks, and enhance decision-making.
Real Estate Researchers & Academics aiming to expand their analytical toolkit by applying Python to large datasets, property market trends, and scenario modelling.
Valuation & Advisory Professionals looking to build more robust models and deliver deeper insights in client presentations.
Early-Career Real Estate Professionals & Graduate Students who want to future-proof their skillset by combining traditional financial modelling with data analytics and visualisation
Who this course is for
Real Estate Analysts who want to move beyond Excel into Python for faster, more advanced financial modelling, cash flow analysis, and sensitivity testing.
Investment Managers & Asset Managers seeking to integrate Python with existing workflows to streamline reporting, automate repetitive tasks, and enhance decision-making.
Real Estate Researchers & Academics aiming to expand their analytical toolkit by applying Python to large datasets, property market trends, and scenario modelling.
Valuation & Advisory Professionals looking to build more robust models and deliver deeper insights in client presentations.
Early-Career Real Estate Professionals & Graduate Students who want to future-proof their skillset by combining traditional financial modelling with data analytics and visualisation
Homepage

https://www.udemy.com/course/python-fundamentals-for-real-estate

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