Provisioning Engineer

Springfield, Ohio rental market, fall 2026: prices, rents, and what the jobs news means. Every quarter I pull the numbers on Springfield, Ohio for the investors I work with, because the story here changes faster than the national headlines suggest. This is the fall 2026 edition: prices, rents, days on market, the renter share, and the jobs news that is starting to show up in tenant demand. If you are deciding whether Springfield belongs on your list, this is the page to read first. Springfield, Ohio by the numbers (updated September 2026). * Median sale price, mid-2026: $214,717, up 7.6% year over year, with 34 days on market and about 2.2 months of supply (WHIO / Redfin). * Median price 2022 | 2026: $140,750 | $220,200 - a 56.5% climb in four years (WHIO, Q1 2026). * Average apartment rent, August 2026: $1,100 (+6%). One-bedroom $904, two-bedroom $1,183, three-bedroom $1,485 (RentCafe). * About 48% of Springfield households rent rather than own (RentCafe). * Typical home value on Zillow: $190,723, and homes go pending in a median of 10 days (Zillow). * Jobs: Vultr's $1.3 billion data center opening in 2026, Silfex's 400-job plant, and roughly 1,400 Honda workers commuting from Clark and Champaign counties. A note on the math: at a $190,000-$215,000 median with $1,100-$1,180 rents, the gross rent-to-price ratio is roughly 0.55-0.6%. That is not where investors make money here. The starter-home tier - sub-$150,000 houses on the north and south sides - is where the ratio climbs toward 0.85-0.9%. That figure is derived from the medians above, not a published statistic; the point is that Springfield cash-flows at the low end of the market, and knowing which sub-$150k houses are worth owning is the whole job. What the price numbers are telling you. A 56% rise in the median since 2022 sounds like a market that has run. It has, at the median. But the median is being pulled up by the surrounding-town and newer-construction end of the market; the sub-$150,000 house on a north-side street has moved far less in dollar terms, and that is the house an investor buys. Days on market at 34 and a median of 10 days to pending say the same thing from the other direction: correctly-priced houses sell fast, and an investor who waits for the perfect deal to sit on the market will not see many. What the rent numbers are telling you. Rents up 6% in a year, to about $1,100 average, with three-bedrooms near $1,485. Compare that to Dayton at $1,110 and Columbus at $1,366: Springfield rents are within a few dollars of Dayton on a substantially lower purchase price. That spread - Dayton-level rents, sub-Dayton prices - is the entire investment case for the city, and it is why the best areas for investors are the ones where that spread is widest. The jobs story, and why it matters for landlords. Springfield has spent thirty years losing population - Clark County is down from roughly 147,000 in 1990 to about 135,000 today (USAFacts). That is the bear case, and it is real. The bull case is that 2025-2026 brought the first serious inbound investment in a generation: Vultr's $1.3 billion data center, Silfex's 400-job plant, the Crusoe data center, and Honda's continued draw from Clark and Champaign counties. Data centers do not employ thousands of people, but they bring construction crews, contractors, and a tier of technical jobs that did not exist here before, and the city is spending $75 million on service expansions to support them. For a landlord the translation is simple: more people with steady paychecks looking for a decent three-bedroom, in a city where half of households already rent and new supply is minimal. I am not forecasting a boom. I am saying the demand floor is firmer than it was two years ago. Where the cash flow is right now. * North and south side single-family, sub-$150k: the buy-and-hold core. Rent-to-price toward 0.85-0.9% on a renovated house. Stable tenants, predictable maintenance. * Older downtown-adjacent houses: the value-add and BRRRR territory. Cheapest entry, biggest rehab, widest ARV gap - and the appraisal risk that comes with it. * Two-to-four unit buildings: thin inventory, strong demand, and the best fit for a house hack. When one comes up correctly priced it does not last. * Enon, New Carlisle, Medway: lower yields, longer tenancies, better appreciation from Dayton and Wright-Patterson commuters. Pay more, turn over less. What I would not do in Springfield right now. Buy at the median expecting cash flow. A $215,000 house renting for $1,300 does not work here, and the data sites that show Springfield as a negative-cash-flow market are computing exactly that. Buy below $150,000, rehab to the block, and the picture changes completely. That is also why I would not buy sight-unseen from a turnkey provider marking up north-side houses to out-of-state buyers; the margin they are charging is the margin you would keep with a local agent. Springfield rental market FAQ. Is Springfield, Ohio a good place to invest in real estate in 2026? At the sub-$150,000 end, for cash flow, with a local team: yes. At the median, for appreciation: it is a slower, less liquid market than Columbus or Dayton, and you should expect that. How does Springfield compare to Dayton for investors? Rents are within a few dollars of each other; Springfield purchase prices are meaningfully lower; Dayton has more inventory and more liquidity. Many of my clients hold both - Springfield for yield, the Dayton suburbs for appreciation. How often is this post updated? Quarterly. The numbers above are as of September 2026 and every figure links to its source. Let's talk about your next Springfield deal. If you want these numbers applied to an actual property instead of a city average, send me your buy box. I'm Jenny Craven, an investor-friendly Realtor with eXp Realty in Springfield, Ohio. Call or text (440) 567-7961 or email [email protected] with your strategy and budget, and I'll send you the areas - and the actual streets - that fit it.

Enough with the bad benchmarks: tools for production-grade research. Qdrant Labs September 01, 2026 On this page: Real world vector search workloads are increasingly large and complex. Enterprises are not using vector search to occasionally search through a couple of PDF files. They are indexing and searching billions of vectors at thousands of requests per second (RPS) and sub 50ms tail latency. Large enterprises also can't tolerate faulty assumptions. Too many benchmarks use gated, proprietary managed services. And even worse, the data is synthetic, the queries are hidden, and the engines are locked behind paywalls. These benchmarks show a 90% recall @ 10 on a purely synthetic RAG benchmark for a production claim. But for an e-commerce or marketplace company feeding top 1000+ results into a second-stage reranker, it's not good enough. Engineers within the world's leading search teams demand reproducibility, 95%+ recall, large retrieval depth, high throughput, and sub 100-ms p99 latency. And so do Qdrant. Why hasn't this problem already been solved? Because generating benchmark datasets at billion-scale is incredibly challenging, and calculating exact ground truth queries requires vast compute and potentially quadrillions of brute-force distance computations. But Qdrant like hard problems. So Qdrant went after it. In partnership with Vultr, Qdrant absorbed the economics of extreme-scale embedding generation and brute-force KNN to provide the scientific and engineering communities with a new standard of search benchmarking datasets: Qdrant-FineWeb-10B. This behemoth of a dataset contains ~25 TiB of vector data alone, consisting of dense and sparse vectors generated from gte-multilingual-base. Then, using its GPU-native benchmarking engine, Qdrant computed the exact top-1000 ground truth for 120,000 dense, sparse, and filtered queries - over a quadrillion distance computations across the full 10B document corpus. Because the industry currently lacks datasets that translate well to multimodal formats and complex filtering, Qdrant is also releasing PubMed-Multi-Vector and Coyo-Vector-Embeddings. These datasets provide the community with the dense, sparse, and multimodal representations that actually reflect modern production architectures. To ensure these datasets aren't just another proprietary vendor claim, Qdrant is open-sourcing the tooling that Qdrant used. Supernova is its high-performance, distributed benchmarking framework designed to make massive-scale dataset generation and brute-force ground-truthing accessible, reproducible and even more cost-effective. The era of relying on 1-million vector datasets, production hearsay, and synthetic approximations is over. Here is the real data, the real ground truth, and the open-source infrastructure you need to run it yourself. Qdrant-FineWeb-10B: benchmarking at internet scale. Qdrant-FineWeb-10B represents the core of this release. It is the largest open-source vector search benchmark available to the community, comprising 24.47 TB of vectors and 28.66 TB of source text and metadata. Created in collaboration with Vultr using gte-multilingual-base on Hugging Face's FineWeb corpus, Qdrant utilized Supernova to compute exact top-1000 brute-force ground-truth nearest neighbors for 100,000 queries across the entire 10-billion vector space. Taken together, this amounted to over one quadrillion distance calculations run in parallel on GPU-accelerated hardware. Additional community datasets. To showcase Supernova's versatility across modalities and provide further assets to the community, Qdrant used Supernova to generate two additional open datasets: | Dataset | Model | Data Type | Vectors | Ground Truth | | Qdrant-FineWeb-10B | gte-multilingual-base | Text | 10.07B dense, 10.07B sparse | Dense, sparse, filtered | | PubMed-Multi-Vector | BGE-M3 | Text | 23.9M dense, 23.9M sparse, 8.37B multi-vector tokens | Dense, sparse, multi-vector | | Coyo-Vector-Embeddings | Qwen3-VL-Embedding-2B | Text & Images | 15.4M dense (2048-dim) | Dense | * PubMed-Multi-Vector: Designed to benchmark hybrid retrieval methods with corpus variables held constant. It generates dense, sparse, and ColBERT-style multi-vector representations over the exact same text corpus, accumulating over 8.37 billion multi-vector tokens across nearly 35 TB of data. * Coyo-Vector-Embedding: Focuses on multimodal retrieval, leveraging a 2048-dimensional vision-language encoder (Qwen3-VL-Embedding-2B) to project image-caption pairs from the LLaVA dataset into a unified shared embedding space. This represents a highly-modern workload that leverages state of the art embedding generation and model architectures. Qdrant plan to continue to release more datasets for the community. Supernova: the open-source benchmarking engine. Qdrant didn't want to stop at releasing a static dataset. Qdrant built Supernova as a free, fully open-source framework so that the broader community can generate, manipulate, ground-truth, and benchmark internet-scale datasets on their own infrastructure, without relying on Qdrant or any third-party stack. Supernova automates the four core phases of building and running a vector search benchmark: 1) embedding generation, 2) brute-force ground-truth calculation, 3) database loading, and 4) evaluation benchmarking. Each phase is driven by a specialized module configured entirely via YAML files and designed for massively parallel execution: * nova-embed (Modular Embedding Pipeline): Unifies disparate backends (SentenceTransformers, FastEmbed, OpenAI APIs) and storage systems (Hugging Face, S3, Cloudflare R2). It operates statelessly without a central database - each worker uses its rank and world size to partition input data independently, achieving linear scaling across cloud and HPC environments. * nova-bf (GPU-Native Ground Truth): Computes exact brute-force top-k nearest neighbors across dense, sparse, and multi-vector representations without running out of memory. It streams data partitions from remote storage, uses custom fused GPU kernels for late-interaction scoring, and evaluates filters early on the CPU to prune irrelevant rows before GPU transfer. * nova-load & nova-storm (Ingestion & Stress Testing): Handle downstream evaluation across backends such as Qdrant, Milvus, and Elasticsearch. nova-load drives parallel ingestion to test write throughput, while nova-storm runs search workloads to track QPS, latency distributions (p50,p95,p99), build times, and recall accuracy against nova-bf ground truth. Overview of the Supernova framework: a standardized pipeline for vector search benchmarking. Each module is designed to scale linearly across cloud and HPC environments, enabling reproducible benchmarking at internet scale. Distributed compute with SkyPilot. To scale compute seamlessly across distributed infrastructure, Supernova integrates nova-dist, a controller-only module built on SkyPilot that handles cluster provisioning, job scheduling, fault tolerance, and cloud abstraction. Rather than hardcoding infrastructure logic into individual pipeline modules, nova-dist decouples job execution from hardware management. This allows nova-embed, nova-bf, nova-load, and nova-storm to scale linearly across AWS, GCP, Azure, Kubernetes, and Slurm HPC clusters using identical YAML configurations - massively parallelizing workloads across hundreds of GPUs without manual infrastructure overhead. Acknowledgements. Qdrant extend its sincere thanks to Vultr for providing the raw compute infrastructure to generate the initial FineWeb-10B embeddings, as well as the SkyPilot and Hugging Face teams for building open-source foundation tools that enable operating at this scale.