Should I Build It Should I Build It
19 Aug 2025
Artificial Intelligence

property search based on condition that scrapes street level data

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Freemium

People love using similar products but resist paying. You’ll need to either find who will pay or create additional value that’s worth paying for.

Should You Build It?

Build but think about differentiation and monetization.

Your are here

You're entering the property search market, which already has several players employing AI and advanced data analysis, as indicated by the 11 similar products we identified. This suggests a competitive landscape where differentiation is key. These products tend to have medium engagement, indicating that while people are interested, capturing their sustained attention requires a compelling value proposition. The positive discussions around similar products highlight the desire for streamlined, intuitive house-hunting experiences, while criticisms point to unmet needs like broader geographic coverage and comprehensive data integration. Given this context, your success hinges on providing unique value, potentially through superior data or a novel user experience that addresses the shortcomings of existing solutions. The 'Freemium' label suggests that users may be hesitant to pay upfront, so creating a compelling free offering that draws them in and showcases your product's value is paramount.

Recommendations

  1. Focus on a niche within property search. Instead of trying to compete with Zillow or Redfin directly, identify a specific user group or property type where your street-level data offers a unique advantage. For example, focus on properties with specific architectural styles or investment opportunities in up-and-coming neighborhoods. This will help you stand out and attract early adopters.
  2. Given that similar products face criticism regarding limited geographic availability, prioritize expanding your data coverage. Start with a specific region and demonstrate success there, but have a clear plan to scale to other areas based on user demand and data availability. User feedback from similar products suggests that expansion to other countries and integration of school catchment area data are highly desirable.
  3. Consider that the idea category is 'Freemium,' you need to identify the core value proposition that can be offered for free to attract users. This could be a limited number of searches per month, access to basic property data, or a simplified version of your street-level data analysis. Use the free version to showcase the power of your full product.
  4. Develop premium features that justify a paid subscription. This could include unlimited searches, access to more detailed property data (e.g., historical price trends, neighborhood demographics), or advanced analysis tools (e.g., investment potential scores, risk assessments). User interest in future property value trends indicates a potential area for premium features.
  5. Explore potential B2B partnerships with real estate agents or investors. Your street-level data could be valuable to these professionals, and they may be willing to pay for access to your premium features. Consider offering a team-based subscription model, as suggested in the idea category recommendations.
  6. Gather user feedback early and often. Use surveys, interviews, and analytics to understand how users are interacting with your product and identify areas for improvement. Pay close attention to user requests for new features or data sources, as this can inform your product roadmap. Given that similar products face concerns regarding their pricing model, make sure to provide clear and transparent pricing details.
  7. Emphasize the unique insights derived from your street-level data. Don't just present the data; tell a story with it. Help users understand how this data can help them make better decisions, whether they're buying, selling, or investing in property. As one comment noted, focus on insights, not just technologies.

Questions

  1. What specific types of street-level data will you be scraping, and how will you ensure its accuracy and reliability? What are the legal and ethical considerations associated with scraping this data, and how will you address them?
  2. How will you differentiate your property search engine from existing platforms like Zillow or Redfin, particularly in terms of user experience and data presentation? What unmet needs in the property search market will your product address, and how will you measure your success in meeting those needs?
  3. What is your plan for user acquisition and retention, given that similar products face competition and a reluctance to pay upfront? How will you create a 'sticky' product that users will want to use regularly, and how will you convert free users into paying subscribers?

Your are here

You're entering the property search market, which already has several players employing AI and advanced data analysis, as indicated by the 11 similar products we identified. This suggests a competitive landscape where differentiation is key. These products tend to have medium engagement, indicating that while people are interested, capturing their sustained attention requires a compelling value proposition. The positive discussions around similar products highlight the desire for streamlined, intuitive house-hunting experiences, while criticisms point to unmet needs like broader geographic coverage and comprehensive data integration. Given this context, your success hinges on providing unique value, potentially through superior data or a novel user experience that addresses the shortcomings of existing solutions. The 'Freemium' label suggests that users may be hesitant to pay upfront, so creating a compelling free offering that draws them in and showcases your product's value is paramount.

Recommendations

  1. Focus on a niche within property search. Instead of trying to compete with Zillow or Redfin directly, identify a specific user group or property type where your street-level data offers a unique advantage. For example, focus on properties with specific architectural styles or investment opportunities in up-and-coming neighborhoods. This will help you stand out and attract early adopters.
  2. Given that similar products face criticism regarding limited geographic availability, prioritize expanding your data coverage. Start with a specific region and demonstrate success there, but have a clear plan to scale to other areas based on user demand and data availability. User feedback from similar products suggests that expansion to other countries and integration of school catchment area data are highly desirable.
  3. Consider that the idea category is 'Freemium,' you need to identify the core value proposition that can be offered for free to attract users. This could be a limited number of searches per month, access to basic property data, or a simplified version of your street-level data analysis. Use the free version to showcase the power of your full product.
  4. Develop premium features that justify a paid subscription. This could include unlimited searches, access to more detailed property data (e.g., historical price trends, neighborhood demographics), or advanced analysis tools (e.g., investment potential scores, risk assessments). User interest in future property value trends indicates a potential area for premium features.
  5. Explore potential B2B partnerships with real estate agents or investors. Your street-level data could be valuable to these professionals, and they may be willing to pay for access to your premium features. Consider offering a team-based subscription model, as suggested in the idea category recommendations.
  6. Gather user feedback early and often. Use surveys, interviews, and analytics to understand how users are interacting with your product and identify areas for improvement. Pay close attention to user requests for new features or data sources, as this can inform your product roadmap. Given that similar products face concerns regarding their pricing model, make sure to provide clear and transparent pricing details.
  7. Emphasize the unique insights derived from your street-level data. Don't just present the data; tell a story with it. Help users understand how this data can help them make better decisions, whether they're buying, selling, or investing in property. As one comment noted, focus on insights, not just technologies.

Questions

  1. What specific types of street-level data will you be scraping, and how will you ensure its accuracy and reliability? What are the legal and ethical considerations associated with scraping this data, and how will you address them?
  2. How will you differentiate your property search engine from existing platforms like Zillow or Redfin, particularly in terms of user experience and data presentation? What unmet needs in the property search market will your product address, and how will you measure your success in meeting those needs?
  3. What is your plan for user acquisition and retention, given that similar products face competition and a reluctance to pay upfront? How will you create a 'sticky' product that users will want to use regularly, and how will you convert free users into paying subscribers?

  • Confidence: High
    • Number of similar products: 11
  • Engagement: Medium
    • Average number of comments: 5
  • Net use signal: 25.3%
    • Positive use signal: 25.3%
    • Negative use signal: 0.0%
  • Net buy signal: 0.0%
    • Positive buy signal: 0.0%
    • Negative buy signal: 0.0%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

Similar products

Relevance

Jitty - 🔍 The first AI-powered photo search for homes

12 Mar 2025 Web App Social Media Home

Jitty revolutionises home search with AI. Search how you think - by features, style, and vibes. Whether it’s ”homes under £2m that look like a castle” or “instagrammable bathrooms”, Jitty finds it. Discover homes effortlessly - just type, search and explore.

Jitty is praised as a revolutionary and game-changing product that simplifies and improves the house-hunting experience with its AI-powered photo search and natural language capabilities. Users find it fun, intuitive, and effective, especially compared to traditional methods. Key features include aspirational browsing and time-saving search. There are requests for expansion to other countries like Italy, Sweden, and France, and for additional features like rental listings and integration of data for school catchment areas. The team and product are consistently lauded.

Users criticize the UK property search experience as difficult and polluted, suggesting integrating Locrating data into a single app. There's a request to exclude selling property shares. Some find existing web portals irrelevant and express pain points. Users suggest expanding to rental listings to save time. The unaffordability of houses with pools is also noted, with some observing that other property apps are generally lacking and need improvements.

daniel_wendon

AI search has meant I can now quickly quickly search for whatever I can think of without building complex filter. And who has a filter for "copper bath in disco bathroom" anyway

begona_fernandez

I use Jitty to day dream about lovely houses and the AI search is the perfect tool to find the quirky property, the Victorian fireplace or the most beautiful sofa next to a staircase possible. Easy to use and then filter the results. And the image carousel just makes the final browse beautiful

ttrauser

Congrats on the launch. It so much easier and intuitive to find homes with Jitty, I hope it launches in Italy soon!

evangelo_panagi1

If you can marry this up with the data Locrating do (I won't have to use 2 apps to find my next house), in particular school catchment, affluence of area, crime etc.

sammorrison

Searching for a house has always been a huge stress. The jump from scanning the windows of estate agent branches to find your dream home, to searching on web portals was a big leap... but it also introduced a tonne of pain points. Now you need to know the entire market and browse 99 irrelevant properties to find one that might work for you. I’ve followed Jitty since first teased and it’s been great to use and see how far it’s come so quickly. Truly excited to see someone making the start of the home purchase journey less stressful! Super intuitive and genuinely helpful 🚀

tom_savage

Property porn needs a new home and this is it.I love Jitty for helping me scroll through all the lives I could be living right now ;)

kwok_c

I've been using Jitty for months and it's anincrediblylovable and sticky product – I love it. Well done team!As a younger person who likely isn't going to be home-buying in the next few years, I wonder whether you have any intention to expand to rental listings? (This would save me sooo much time in looking for my next apartment 🤞)

aymancork

Been testing this out for a few months now and am enjoying seeing all of the houses with a swimming pool I can't afford across the country 😁 It's so quick and all of the other filters make house hunting simpler than the competitors on the market. Well done to the team behind this, it's been evolving so much recently!

george_nichkov

I’ve been using Jitty since its early beta, and it’s truly designed with the user in mind – easy to use, beautifully crafted, and minimalistic, yet totally transparent. It gives you the full picture of home costs upfront, eliminating the need to look elsewhere. The smart search, with its powerful filtering (and there are plenty!), is simply brilliant. House hunting has never been this easy and enjoyable.Best of luck to the team! 🚀🤞🏻@jamesstorer

hayley_marie_hoy

This looks amazing, I really wish we'd had this whilst looking for our first home 🏡 but happy if we want to move Jitty are the ones to help get our next dream

eeshita_pande

Awesome product, fun and intuitive to use, photo-based search is super awesome

jonnyburch

Great work on this. Every time I've bought (or looked for) a house it's been such a waste of time looking at houses that just aren't right. Can't wait to use Jitty for my next move!

zoe_knight

Love this idea! Will make exploring so much easier and less complex! Using AI in photo home searching is pretty cool!

nowyv

For sure checking this out!

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I got tired of manual property research,so I built Z-Scraper,free trial

13 Oct 2024 SaaS

Tired of manual Zillow searches, I built a web-based scraper.As a real estate investor, I found myself constantly sifting through Zillow listings, manually copying data into spreadsheets. It was mind-numbing work that ate up hours of my time. I knew there had to be a more efficient way.I realized this wasn't just my problem. Many real estate professionals – from agents to investors to market analysts – were likely facing the same tedious task. We all needed a quick, reliable way to gather property data at scale.Over six weeks of intense coding (and more coffee than I care to admit), I developed Zillow Scraper. The journey wasn't without its challenges – I had to navigate Zillow's data structure, ensure accurate parsing, and create a user-friendly interface. But the result was worth it.Zillow Scraper simplifies the entire process of collecting property data. Here's what it does:1. Just input a city or area, and it scrapes all relevant listings 2. Export data directly to Excel (.xls) or CSV formats 3. No plugin or software installation required – it's entirely web-based 4. Provides 3 free searches for new users to try before committingThe tool has streamlined my own investment research, and now 300 active users are benefiting from it too. Real estate agents are using it to quickly gather comps, investors are efficiently analyzing new markets, and analysts are effortlessly collecting data for trend reports.I'd love for the HN community to give it a try and share your thoughts. What additional features would make this even more useful for you? Are there any other data points you'd like to see included in the exports?You can check it out at https://zillowscraper.siteRemember, you get 3 free searches to test it out – no credit card required.Looking forward to your feedback and suggestions!

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Natural language search for real estate with lots of filters

08 Apr 2024 Artificial Intelligence

I've been working on a project that lets you search for properties in London (UK, not Ontario) with a huge number of filters.Property agents in England typically put very little effort into writing useful listings and the dominant search engine, Rightmove, has barely changed in 20 years. So you have to collate information from a lot of sources – and it takes a lot longer than it should – to find properties that satisfy your criteria.Currently my search engine does a decent job of finding matching properties, but there are so many filters that there’s a big discoverability issue.So I built this natural language interface as a first attempt at addressing the problem. You type in things like “2-bed garden flats over 800 sq ft with lots of storage, away from major roads, in safe areas with good primary schools, max price 850k, max 45 mins by tube to Buckingham Palace” and the tool will spit out properties that match your criteria.There’s a lot more to do to make it truly usable: it doesn’t always understand what you’re searching for, it only supports a small subset of the main website’s filters and there’s plenty of work to do on the UI (e.g. making filters editable).Most importantly the current incarnation doesn’t even address the discoverability issue. Currently I’m thinking this could work quite well as a back-and-forth conversation with a “virtual realtor” who guides you through the search a bit, but that’s TBD.Anyway, let me know what you think! I’m especially curious as to how it stacks up vs. the information-dense UI on the main website: https://findmyarea.co.uk/

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Camphor Property Search - AI-enhanced property search to improve homebuying

07 Nov 2023 Search Artificial Intelligence Home

Homebuying is an incredibly personal experience. No two people will have the exact same criteria. With Camphor, you can search for properties by walkability, safety, and more beyond traditional criteria. We've launched in the Bay Area, CA and New York City!

The Product Hunt launch is receiving positive feedback, with users congratulating the team and expressing excitement. One user inquired about pricing details. Another user highlighted the product's functionality in the SF Bay Area and NYC. There's also mention of potential future value trends related to Camphor Property Search.

The primary criticisms focus on limited geographic availability, with the product largely restricted to the SF Bay Area and NYC. Users also expressed concern over the absence of pricing information. A suggestion was made to enhance the product by incorporating estimated future property value trends, based on available data, to provide users with more comprehensive insights.

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Hands-On Data Engineering with a Real-Estate Project Guide

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Hey HN community,I recently pushed an update to my GitHub repo titled "Practical Data Engineering: A Hands-On Real-Estate Project Guide". This open-source project aims to tackle real-world data engineering challenges while exploring various technologies. It guides you through building a data application that collects, enriches, and visualizes real-estate data, potentially helping you find your dream property.This project covers web scraping with Beautiful Soup, processing data with Spark and Delta Lake, visualizing with Apache Superset, and much more, all orchestrated on Kubernetes for scalability.I started this project back in November 2020, mainly to learn and teach data engineering. Three years on, I'm fascinated by the fact that despite the data engineering space moving extremely fast, the core of my project, powered by carefully chosen tools from the Open Data Stack, remains relevant to this day. This project is my most searched blog post on Google, which motivated me to update it.I updated to the latest versions of tools like Dagster while exploring new additions like delta-rs, which allows direct interactions with Delta Tables in Python.https://github.com/sspaeti-com/practical-data-engineeringI look forward to your thoughts and seeing what you would build differently. My future plans are to add Rill Developer as a code-first BI tool and add DuckDB or Polars to the mix.

Focus on insights, not just technologies.

Highlighting technologies over insights.

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Leveraging AI for Home Search

10 Jul 2024 Search Artificial Intelligence Home

Hey HN!We've been exploring how AI/ML can play a role in improving a home buyer's experience, and we're excited to show off our take on home searching.Why did we build it? Zillow and similar platforms are limited by the usual filters: location, price, and number of bedrooms. What if you wanted to search for homes with more natural light, on a quiet street, with high ceilings, a fenced yard, near parks and trails—all the features that really matter to your lifestyle? We believe that there is a meta problem here of matching people to places, and we've gone above the home level as well, using this kind of intelligence to learn preferences and match neighborhoods and locations.We've developed a novel strategy of ingesting home listings so that they can actually be used in retrieval systems. From there, we use a number of SOTA techniques to curate recommendations for users. We're currently working to take this beyond search, and build in guidance and reasoning, for more contextual information to the home buyer.Tip: Tell us the most important things you look for in a home in your search criteria, this will help us pressure test the AI. You can also ask followup questions about the home, and general questions about the real estate process.*Also, these search results are limited to Massachusetts only.Give it a go!

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