Rebiha

Home › Datasets › Real Estate

Real Estate Fine-Tuning Dataset

Listing descriptions, buyer/seller communication, and showings.

Fine-tune a model on this dataset

$29 for the dataset. Training is priced separately by model, starting at $47.

What is in this dataset

844 instruction-response pairs covering listing descriptions, buyer/seller communication, and showings. Each pair is a realistic question with a full written answer, in the JSONL format used for supervised fine-tuning. No synthetic filler, no duplicated rows.

Example pairs

Real rows from the dataset, shown in full.

Write an engaging listing description for a 3-bedroom, 2-bath suburban home with an updated kitchen, a fenced backyard, and a finished basement.

Welcome to 123 Maple Lane—a meticulously maintained 3-bedroom, 2-bath retreat in the heart of Oakwood Estates. Step inside to find a freshly updated kitchen boasting quartz countertops, stainless steel appliances, and soft-close cabinetry, perfect for home chefs and gatherings. The open-concept living and dining area flows seamlessly to a private, fully fenced backyard with a patio—ideal for summer barbecues or quiet evenings. Downstairs, the finished basement adds versatile space for a home gym, media room, or guest suite. With hardwood floors throughout, ample storage, and top-rated schools nearby, this home offers move-in-ready comfort. Schedule your showing today and make it yours.

Respond to a buyer who emailed asking, 'Is the 3-bedroom at 456 Oak Street still available? Can I see it this weekend?'

Hi [Buyer Name], thanks for reaching out! Yes, the 3-bedroom at 456 Oak Street is still available. I have open slots this Saturday from 10 AM to 2 PM and Sunday from noon to 4 PM. Just let me know what time works best for you, and I'll confirm the appointment. Also, feel free to share any specific questions about the property—I'm happy to provide details beforehand. Looking forward to showing it to you!

Explain the home-buying process to a first-time buyer who asks, 'What are the exact steps from offer to closing?'

Great question! Here's a step-by-step breakdown for first-time buyers: 1) Get pre-approved for a mortgage—this shows sellers you're serious and clarifies your budget. 2) Find a real estate agent (that's me!) to guide you. 3) Start house hunting and tour properties. 4) Once you find the right home, we'll submit a written offer with terms like price, contingencies, and closing date. 5) If the seller accepts, you enter escrow—a neutral third party holds funds and documents. 6) Complete inspections (home, pest, radon, etc.) and negotiate any repairs. 7) Finalize your mortgage with the lender. 8) Do a final walkthrough to ensure the home's condition matches the contract.…

Who this is for

Agents and property platforms writing listings and handling buyer questions.

Which models this works with

The dataset is plain JSONL, so it works with any instruction-tuned open model. On Rebiha you can train it directly on:

Training uses LoRA, or QLoRA on a 4-bit base for larger models. Your base model's weights are never modified — training produces a separate adapter.

How it works

  1. Pick this dataset and a base model.
  2. Rebiha runs the job on a managed GPU. You set up nothing.
  3. Download your model: a ready-to-run GGUF, plus developer assets (adapter, tokenizer, configs).

Fine-tuning or RAG?

Use RAG when the model needs facts that change — your prices, your documents, today's inventory. Use fine-tuning when you want consistent style and structure without prompting for it every time. This dataset teaches the shape of a good answer in this domain, not facts to look up.

Related datasets

Start fine-tuning