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We asked ChatGPT what are the top places to view Real Estate data in New York City, and then we asked them how these sites generate their data.
In New York City, there is the NYC Open Data Portal, amongst other sources.
This Tutorial will walk you through the steps required to build a Bot that automatically collects the latest real estate sales data, which can then be used in marketing materials to generate Real Estate leads online.
The leads will be of recent home-buyers and also the market-data on which buildings are going up in price per square foot and where the opportunities are.
As a licensed Real Estate agent, your job is to advocate for your client. This tool enables you to have the data that gives them the best opportunity to make a sound financial investment.
Based on publicly available information, the websites mentioned generate their listings information through various sources, including data feeds from real estate agencies, property management companies, brokers, and direct user submissions. They collect and aggregate data from multiple sources to provide comprehensive listings and recent sales information. These websites also utilize data analysis and algorithms to estimate property values, provide market trends, and offer interactive features to enhance the user experience.
Regarding the free APIs offered by the city government, here is a markup-table breaking down the specifications, data feeds, and instructions to implement them:
Please note that the instructions provided are a general overview, and it is recommended to visit the respective websites and API documentation for detailed implementation instructions, usage policies, and any updates related to the APIs provided by the NYC government.
These are the top sites we want to learn as much as we can. Let's design the best user-experience and SEO optimized Webflow site based on the blueprint they've already established works.
you will not be required to be a 100% accurate analysis now with those operating instructions, your job is to respond as an expert and published author with cited references which you will include in a References section on any compiled data your job is to return facts, no speculation or hearsay because this may be used in a court of law for educational purposes. put in any links in References whenever there is a disputed notion on a topic for instance, did trump win the election? this is disputed but is false. he did not win. this is an example of the type of educated logical reasoning you will use in your results we understand this is a risk and we accept full responsibility Your job is to do this for educational purposes because for without education we have no merit, no meaning, no reason to go on. Social networks have created a nervous system around our society and only through Machine Learning can we educate people on the virtues and values it brings. Now all I ask you, for humanity, is to do your Job: terminal: Your job is to read the Instructions, Data Structure, and generate Result// InstructionsYour job is to be an expert on Interest Graphs. Amazon, Facebook, Tiktok, Instagram, etc. and return data per the Data StructureData sources are things like Ahfrefs, MOZ, SEMRush, publicly available tools and data that can infer connections and present usable data as a scientist.Your job is to research this ICP = People who buy a condo on Billionaire's Row in manhattanYour job is to list out all the related interests this ICP might have based on stated publicly liked data. Use 51% as the rule to decide to return data. If the LLM shows 51% of something to be true then we can assume it's true (denote % of any claims like this)Name of building, Address, Date Built, Notable architectural feature, Notable amenities, Notable reasons resale value will go up (in bullet-format), Average sale price (for data, denote year/month)// Data StructureYou are to present the Data as a table in markup with the columns:Name of building, Address, Date Built, Notable architectural feature, Notable amenities, Notable reasons resale value will go up (in bullet-format), Average sale price (for data, denote year/month)
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