Dynamic Real Estate Comparable Sales Engine

@ASirbescu

Hello, As an experienced Full-Stack Developer, my proficiency in Python and understanding of geographic information systems position me at the vanguard of your project. I assure efficient development of your dynamic Comparable Sales Engine that incorporates advanced functionalities such as pulling geographically relevant comps, filtering by size similarity, and adjusting for property conditions (new vs non-new). With an awareness that a radius must be adjustable per city, per neighborhood, per property density, and override-able manually or via admin settings, my ability to code dynamic radius logic is a perfect fit for the demands of this project. Knowing that each comparable property must be tagged with condition keywords like "fully renovated" or "new construction" and identify rehab gaps across six categories is another area where my expertise excels. I will leverage not only my Python skills but also weighed distance scores, calculated similarity scores to curate a list of 2-5 most relevant comps helping you to analyze your future property. Finally, my determination to deliver superior results consistently is a trait I've instilled within myself over the past years. This is attested by my success in managing numerous complex web and mobile application projects. Your project requires top-notch technics to provide an accurate analysis while considering different city nuances and the adjustments involved which aligns perfectly w Thanks!

@asifalimzd

Hi there, I can build your dynamic Comparable Sales Engine with all the logic you outlined — a configurable system that adapts by city, neighborhood, and property type to deliver accurate ARV calculations. Proposed Approach: - Geographic Radius Logic: Adjustable radius per city/neighborhood with manual override, ensuring tight comps in dense areas like Philadelphia and wider ranges in suburban markets. - Size Similarity Filter: Configurable ±400 sqft range to keep comps relevant. - Condition Classification: Binary tagging (NEW vs NOT NEW) with MLS keyword detection and manual override. - Rehab Gap Categories: Six rehab levels tracked to measure risk and budget, without altering ARV. - Comparable Selection: Filters by radius, size, condition, and recency, then scores similarity to select 2–5 best comps. - ARV Calculation: Average PPSF × target property sqft, with comp list and structured report output. - Deal Type Flexibility: Flips, cosmetic renovations, new construction, and multi‑family scenarios selectable, with filters easily adjusted per deal type. - Admin Console: Override settings, adjust filters, and manage deal types without touching code. I’ve built real estate analytics tools with modular logic and configurable filters, ensuring accuracy across diverse markets. This system will give you precise ARV calculations while remaining flexible for future expansion. Thanks and regards, — Asif Ali Zaman

@Webtechfusion23

Hi, ➡️ I read your project description; you need a dynamic Comparable Sales Engine tailored for varying real estate markets, focusing on Philadelphia and adaptable to other cities. This tool will dynamically adjust comp radii, filter properties by size and condition, and calculate ARV based on selected comparables. ⏺️ With over 12 years as a Full Stack Developer, I specialize in developing complex data-driven applications. I have extensive experience in geospatial algorithms, real estate market analysis tools, and adaptive user interfaces. My approach will ensure that your engine is scalable, user-friendly, and precise in its output, adjusting seamlessly to each specified real estate scenario. Regards, Aftab Ahmad Full Stack Developer (12 Years of experience)

@extreamcode

Hi, I understand the nuanced challenge of building a dynamic Comparable Sales Engine that adapts by city, neighborhood, and property specifics like you described for Philadelphia versus suburban markets. Your project demands precise geographic radius configuration, flexible filters for size, condition, and rehab needs, plus a smart comp selection with a robust ARV calculation. I have extensive experience with database management, Python API development, and building real estate analytical tools, enabling me to translate your detailed logic, dynamic radius, condition tagging, rehab categorization, similarity scoring, into a flexible, admin-configurable web system. I propose starting with a clear data model and API framework to pull and filter comps, then iteratively add your rehab scoring and deal-type filters. I’ve shared an initial estimate based on your description, and once we go over a few technical or functional details, I’ll confirm the exact cost and delivery schedule. Let’s discuss your preferred tech stack and any existing data sources. Could you share which data sources or MLS APIs you currently use or plan to use for comp data? Thanks, Asad

@denisSolution

Hello client, I'm Denis Redzepovic, an experienced developer with expertise in Python, Database Management, API Development and Web Development. I have worked extensively on diverse Python projects, ranging from backend development and automation to data processing and API integrations. My deep understanding of Python’s libraries and frameworks allows me to build efficient, scalable, and maintainable solutions. I pay close attention to code quality and performance to ensure your project runs flawlessly. With my solid experience, I’m confident I can deliver results that exceed your expectations. I focus on writing clean, maintainable, and scalable code because I know the difference between 99% and 100%. If you hire me, I’ll do my best until you’re completely satisfied with the result. Let’s discuss your project details so I can tailor the perfect Python solution for you. Thanks, Denis

@Azursol

Dear Client, I am an experienced real estate data analyst with a proven track record of developing dynamic tools to solve complex market challenges. I understand the need for a robust Comparable Sales Engine that can accurately assess property values based on specific criteria. My approach includes pulling geographically relevant comps, filtering by size similarity, adjusting for property condition, scoring rehab gaps against ARV comps, selecting the most relevant comps, and calculating ARV using averaged $/sqft. To address your specific requirements, I will implement a configurable system that allows for dynamic radius adjustments, size similarity filters, condition classifications, rehab gap classifications, comparable selection logic, and ARV calculation logic. This system will provide you with accurate and actionable insights to make informed real estate decisions. I invite you to discuss this project further to explore how my expertise can add value to your business. Best regards,

@saifsolutions

Hi, Your project, "Dynamic Real Estate Comparable Sales Engine," immediately caught my attention. I’ve reviewed your description carefully, and as a creative designer with extensive experience in Web Development, I’m confident I can deliver a solution that meets your expectations and aligns with your vision. Check out my profile here: ✨ https://www.freelancer.com/u/saifsolutions ✨ Feel free to reach out via chat or Freelancer call so we can discuss your project in more detail. Best regards, Saifullah

@maximg10

Hello. Thanks for your job posting. ⭐Dynamic Real Estate Comparable Sales Engine⭐ I'm the developer you're looking for. I can successfully complete your project. Let's chat for a more detailed discussion. Thank you. Maxim

@abhisaini0188

Hello, I am really excited about the opportunity to collaborate with you on this project! It aligns perfectly with my skill set and experience, and I’m confident I can contribute meaningfully to your vision. I genuinely enjoy working on projects like this, and I believe we can create something both functional and visually engaging. Please feel free to check out my profile to learn more about my past work and client feedback. I’d love to connect and discuss the project details further your goals, expectations, and any specific features or ideas you have in mind. The more I understand your vision, the better I can bring it to life. I am ready to get started right away and will put my full energy and focus into delivering quality results on time. My goal is not just to complete the project, but to exceed your expectations and build a long-term working relationship. Looking forward to hearing from you soon! With regards! Abhi

@DngPhgNam

⭐⭐⭐⭐⭐ ✅Hi there, hope you are doing well! I have deep experience building dynamic real estate valuation and comparable sales engines that pull precise comps with adaptable geographic and size filters, similar to your detailed ARV calculation system. The key to success in this project is creating a flexible, adjustable comp selection engine that can dynamically adapt radius, square footage range, and condition filters by city, neighborhood, and property type. Approach: ⭕ Develop a configurable API backend to pull comps by dynamic radius logic customizable per city and density. ⭕ Implement flexible size filters and condition classification with manual override options. ⭕ Build rehabilitation gap scoring and deal-type filters for flips and renovations. ⭕ Design a similarity scoring algorithm to select 2-5 comps while allowing radius expansion as needed. ⭕ Calculate ARV through averaging $/sqft and output a clear report including comps used. ❓ Could you clarify what data sources or MLS feeds will be integrated for property details and sold comps? ❓ What is your preferred tech stack for the web and database layers? ❓ Do you require a front-end dashboard or just an API/service? I am confident I can deliver a robust, configurable Comparable Sales Engine tailored to your requirements on time and within scope. Thanks, Nam

@webperfection123

Hi, I can design a dynamic system that pulls geographically relevant comps while allowing for adjustable parameters based on city and neighborhood. The engine will filter for size similarity and condition, identify rehab needs, and calculate the ARV using an average price per square foot. With 8+ years of experience in real estate data systems, I can ensure that the engine is user-friendly and maintainable. I will incorporate your requirements to allow for easy manual adjustments to enhance functionality. Let’s discuss how we can bring this project to life and customize it to your needs. Best Regards, Priyanka

@Ahmedh058

Hi there, I’m Ahmed from Eastvale, California — a Senior Full-Stack & AI Engineer with over 15 years of experience building high-quality web and mobile applications. After reviewing your job posting, I’m confident that my background and skill set make me an excellent fit for your project — Dynamic Real Estate Comparable Sales Engine . I’ve successfully completed similar projects in the past, so you can expect reliable communication, clean and scalable code, and results delivered on time. I’m ready to get started right away and would love the opportunity to bring your vision to life. Looking forward to working with you. Best regards, Ahmed Hassan

@jayprakashyadav

As a professional full-stack developer, I bring a plethora of technical expertise to the table that aligns perfectly with your project's needs. I have extensive experience in web and mobile app development using a range of technologies such as Flutter, React Native, Kotlin and Swift -- all of which can be vital for delivering a dynamic, real-time Real Estate Comparable Sales Engine. Moreover, given the project's core requirement to provide geographic relevance in terms of property comps, my mastery in database management (ensuring data is up-to-date) and integrating REST APIs will contribute to efficient data retrieval. Customizing databases fittingly for cities, neighbourhoods and densities has been a significant part of my previous projects as well. Finally, the configurability aspect of your project with regards to sizes, condition classifications and ARV calculations is something I prioritize on all my developments. I understand the value of scalability and future-proofing your system. So, if you choose me for this project, not only will you get someone who can meet your current requirements but also adapt effectively to future demands and iterations of your sales engine. Thanks, Jay

@chrissiebosman

As an Automation and AI developer with expertise in Python, I am uniquely positioned to design, build, and fine-tune your Dynamic Real Estate Comparable Sales Engine. A project of this nature requires a high level of logic and adaptability - which my experience in process automation and AI development brings to the table. My understanding of the intricate logic involved in pulling geographically relevant comps and filtering them based on size similarity will ensure that your engine returns the most appropriate data for each targeted property analysis. Additionally, my experience in working with similar condition classification methodologies such as identifying MLS keywords or manual override fields will be instrumental in helping us accurately tag the condition of each comp. Moreover, my capability to handle complex data engineering tasks will be key in calculating ARV using averaged $/sqft method and building intricate configurable systems adjustable by city, neighbourhood, and property density among other parameters. My solution-oriented approach is backed by robust skills in Python, Django, FastAPI and more – meaning your project will receive a solution that is not only smart but also future-proofed for scalability. Let's help you outshine the real estate market competition together!

@jonathangleon

Hi — I can build this Comparable Sales Engine end-to-end with city/neighborhood configurability, clean scoring logic, and outputs that match how investors underwrite ARV. What I’ll deliver * Dynamic radius system: defaults by city/density (e.g., Philly 0.25mi) + neighborhood overrides + per-property manual override + auto-expand radius until min 2 comps found. * Filters: sold within X months, sqft ±400 (admin adjustable), property type, deal type (flip/cosmetic/new construction/multifamily tiers), and condition target (renovated vs not new). * Condition tagging: keyword rules (“fully renovated”, “gut rehab”, etc.) + manual override. * Rehab gap scoring (6 categories): compares target vs ARV comps for budget/risk (doesn’t change ARV). * Similarity ranking: weighted Distance + Sqft delta + Condition match; selects top 2–5 comps. * ARV calc: avg $/sqft × target sqft + comp list + avg/median PPSF. Admin config City → Neighborhood → Defaults (radius, sqft range, months, weights, deal-type templates). Easy to adjust later. If you tell me your data source (MLS feed / PropStream / public records / internal comps DB), I’ll wire the pull + caching + reporting view.

@TechnoWorldIT

I can build a dynamic Comparable Sales Engine that pulls geographically relevant comps, filters by adjustable radius, square footage, and condition, scores rehab gaps, and calculates ARV by averaging $/sqft of 2–5 selected comps. The system will support manual overrides for city, neighborhood, property density, and deal type, classify rehab across six categories, handle multiple property types (flips, cosmetic renos, new construction, multi-family), and generate structured reports showing selected comps, similarity scores, PPSF averages, and ARV, all while keeping the workflow configurable and adaptable for different markets.

@ixorawebmob

Hello, I’ve gone through your project details and this is something I can definitely help you with. I have 10+ years of experience in mobile and web app development, working with Flutter, Android, iOS, React, Node.js, and APIs. I focus on clean architecture, scalable code, and clear communication to ensure the project runs smoothly from start to finish. I will first review your requirements, suggest the best technical approach, and then proceed with development while keeping you updated at every stage. Here is my portfolio: https://www.freelancer.in/u/ixorawebmob I’m interested in your project and would love to understand more details to ensure the best approach. Could you clarify: 1. Do you need this for mobile, web, or both? 2. Do you already have UI/UX designs or should we create them? 3. Will there be any third-party API or payment gateway integration? 4. What is your expected timeline for completion? 5. Are there any reference apps or websites you like?How important is it for you to have a user-friendly interface for this dynamic engine? Let’s discuss over chat! Regards, Arpit Jaiswal

@Tanmoy236

Warm Hello! I specialise in building data-driven real estate analysis systems, and with over 9 years of experience, I can architect your dynamic Comparable Sales Engine with configurable ARV logic and deal-type intelligence. Here’s how I can help: Build dynamic radius logic (city, neighborhood, density-based + manual override) Implement adjustable sqft filter (±400 editable) and sold-date window logic Create binary + manual condition tagging (NEW vs NOT NEW) for comps and target Develop rehab gap scoring across 6 categories (risk + budget insight) Design similarity scoring engine (distance, size, condition weights) Auto-select 2–5 best comps with smart radius expansion if needed Calculate Average PPSF, Median PPSF, and ARV with structured report output Add deal-type selector (Flips full gut, cosmetic, New Construction, MF 2–4, 4–9, 10+) with condition-specific comp filters System will be modular and configurable per city and neighborhood. Quick question: What data source will power comps — MLS, PropStream, ATTOM, Zillow API, or internal DB?

@rbtech1984

Hello There!!! ★★★★ ( Dynamic Real Estate Comps Engine ) ★★★★ Project understanding: You need a configurable comparable sales engine that dynamically pulls and scores comps by radius, sqft range, and condition, then calculates accurate ARV. The system must adapt per city, neighborhood, and deal type. ⚜ Dynamic radius by city/density ⚜ Sqft similarity filtering (± adjustable) ⚜ Condition tagging and matching ⚜ Rehab gap scoring (6 categories) ⚜ Smart comp ranking (2–5 selection) ⚜ ARV calculation via avg PPSF ⚜ Admin overrides and deal-type filters With 9+ years experiance in data-driven platforms and real estate analytics, I’ve built valuation and scoring engines that handle complex market logic reliably. I enjoy turning messy property data into clear investment insights. My approach: Python backend with modular scoring engine, fast geo queries (PostGIS), and admin controls for easy tuning. System will be fully configurable and future-proof for new comp types. Happy to review your data sources and start quickly. Warm Regards, Farhin B.